AgentProvenance

<div align="center"> # AgentProvenance ### Correlate application context with system telemetry into a verifiable, signable causality graph for sandboxed agents. Correlate application-side agent context with system-side telemetry, then turn runtime evidence, file diffs, artifacts, risk signals, and response decisions into a queryable, replayable, and auditable causality graph. Evidence is stored content-addressed and hash-verified (a model borrowed from Git) and can be signed for tamper-evidence -- but this is an audit/provenance layer, **not a version-control system**: there is no merge, checkout, or mutable working tree. [](https://go.dev/) [](https://github.com/ByteYellow/AgentProvenance/actions/workflows/ci.yml) [](https://www.docker.com/) [](https://www.sqlite.org/) [](LICENSE) **[Quickstart](#quickstart)** | **[Core Model](#core-model)** | **[Current Capability](#current-capability)** | **[Roadmap](#roadmap)** </div> --- <p align="center"> <img src="docs/assets/agentprovenance-architecture.svg" alt="AgentProvenance architecture: application context and system telemetry enter an ingest boundary, then become a verifiable provenance graph." width="100%"> </p> <p align="center"> <img src="docs/assets/evidence-dag.svg" alt="AgentProvenance evidence DAG: LLM intent, tool call, process, runtime event, policy risk, response, artifact, manifest, and verification." width="100%"> </p> AgentProvenance is a local-first security and provenance control plane for autonomous, tool-using agents, especially sandboxed coding agents. It captures system telemetry from its own eBPF sensor (or ingests Falco/Tetragon), correlates it with app-side agent context into a verifiable, signable causality graph, and serves that graph over the CLI, a daemon API, AI tools (including an MCP server), and a local web dashboard. It is not a generic sandbox runtime, generic telemetry collector, Kubernetes/Ray replacement, RL trainer, trace dashboard, or version-control system (it borrows Git's content-addressing and verification model, not its branch/merge workflow). It owns a narrower primitive: ```text Execution Context -> Evidence Ingest -> Runtime Causality Graph -> Provenance DAG -> State Diff / Blame / Artifact Lineage -> Security Analysis / Risk Decision -> Taint / Response Action -> Replay / Forensics / Audit Manifest ``` The goal is to answer questions ordinary traces do not answer well: - Which snapshot did this execution start from? - Which attempt produced this artifact? - Which tool call started this process? - Which child process caused this runtime event? - Which process changed this file? - Which behavior is anomalous for this agent or task profile? - Which execution branch was tainted, quarantined, interrupted, or blocked by a response gate? - Which evidence supports a risk decision? - What response action should be triggered: audit, deny, kill, quarantine, taint, export forensics, or notify a human through Feishu/DingTalk? - What exact behavior evidence, deviation signal, and risk context should an external evaluator, RL pipeline, or human reviewer inspect? - Can this execution be diffed, blamed, verified, replayed, and audited later? <p align="center"> <img src="docs/assets/evidence-flow.svg" alt="AgentProvenance evidence flow" width="920"> </p> ## Contents - [Why](#why) - [Security Loop](#security-loop) - [Core Model](#core-model) - [White-box mode](#white-box-mode) - [Zero-SDK mode](#zero-sdk-mode) - [Relationship To Existing Systems](#relationship-to-existing-systems) - [Quickstart](#quickstart) - [Deployment Modes](#deployment-modes) - [Falco-compatible Receiver](#falco-compatible-receiver) - [Security Evidence Commands](#security-evidence-commands) - [External Evaluator Protocol](#external-evaluator-protocol) - [Compliance Evidence, Not Certification](#compliance-evidence-not-certification) - [AI-Callable Evidence Tools](#ai-callable-evidence-tools) - [Web Dashboard](#web-dashboard) - [Graph Commands](#graph-commands) - [Current Capability](#current-capability) - [Core Demo Acceptance](#core-demo-acceptance) - [Architecture](#architecture) - [Substrate Signals](#substrate-signals) - [Boundaries](#boundaries) - [Repository Layout](#repository-layout) - [Roadmap](#roadmap) - [Development](#development) ## Why Modern agent execution is not one prompt and one tool call. Coding agents and autonomous workflows fork attempts, edit files, run tests, create artifacts, spawn subprocesses, touch external systems, and trigger runtime telemetry. Logs, traces, metrics, and sandbox events each capture pieces of that story, but they rarely produce a Git-like causal record of execution state. AgentProvenance turns sandboxed execution into a security-oriented evidence graph: ```text base snapshot -> attempt -> execution context -> tool_call -> process / child process -> runtime_event -> file_diff / artifact -> baseline feature / risk signal -> taint / response action -> replay / forensics / audit manifest ``` The primary path is recording and explaining sandboxed agent execution. The branch-heavy coding-agent script is only a stress demo: it creates many branches, artifacts, runtime events, and risk cases quickly enough to exercise the graph. AgentProvenance does not choose the reward winner. It emits structured trajectory evidence and expectation-deviation signals so an external evaluator or training pipeline can turn them into reward, penalty, filtering, or human-review decisions. For RL pipelines, the useful primitive is not "best-of-one" or automatic winner selection. The useful primitive is observability over each trajectory: what the agent did, which subprocesses and files were touched, which network/runtime events appeared, which behavior violated safety or task expectations, and which risk/baseline signals should contribute to reward shaping or rejection. ## Security Loop The security model is intentionally simple and concrete: ```text application context run / session / attempt / tool_call / user / task / workspace system telemetry process / file / network / resource / sandbox / eBPF event correlation container_id / cgroup_id / pid / ppid / cwd / timestamp / file diff security analysis behavior baseline / suspicious event / taint lineage / risk decision response audit / deny / kill / quarantine / taint / forensics / Feishu or DingTalk notification ``` This makes AgentProvenance closer to an AI-era HIDS/control-plane layer than a pure LLM trace dashboard. Traditional host monitoring asks "what did this process do?" AgentProvenance adds the agent execution context needed to ask "which agent/tool/task caused it, what state did it change, what evidence proves it, and what response should happen?" The project currently implements the evidence graph, runtime correlation, diff/blame, telemetry batch manifests, policy decisions, normalized risk signals, baseline deviation records, response action records, taint, quarantine, and forensics/export foundations. Deeper eBPF receivers and Feishu/DingTalk response adapters belong to the next security-control phases. ## Core Model AgentProvenance supports two context modes. ### White-box mode An agent harness, SDK, tool router, or framework explicitly provides context: ```text run_id / session_id / attempt_id / tool_call_id / tool_name / args_hash ``` This gives high precision and fits custom coding-agent systems, Agentix-style harnesses, LangGraph-like workflows, and internal tool routers. ### Zero-SDK mode The user can run an agent command directly: ```sh agentprov record -- <agent command> ``` The current MVP records a command in a working directory, snapshots the pre-execution file state, runs the command, computes post-execution file changes, and emits runtime file evidence into the DAG. The long-term zero-SDK path adds deeper process-tree and kernel telemetry capture. Zero-SDK inference uses runtime facts: ```text root process / process tree / cwd / timestamp / container_id / cgroup_id / file diff / artifact refs ``` Raw system-side telemetry should not be required to carry `tool_call_id`. Kernel and runtime signals usually know PID, cgroup, namespace, container ID, timestamp, and process tree. AgentProvenance correlates those substrate facts back to execution context. Today, the CLI exposes the underlying binding primitive: ```sh agentprov telemetry bind --run <run_id> --session <session_id> \ --attempt <attempt_id> --tool-call <tool_call_id> --process <process_id> \ --container-id <container_id> --cgroup-id <cgroup_id> --pid <pid> ``` Then raw events can be ingested without `tool_call_id`: ```sh agentprov telemetry ingest --raw-event raw-execve-1 --pid <pid> \ --timestamp <event_time> --source tetragon_jsonl --type execve \ --payload '{"argv":["./async_child.sh"]}' agentprov telemetry ingest-jsonl --format tetragon --file tetragon-events.jsonl agentprov telemetry ingest-jsonl --format native --file agentprov-sensor-events.jsonl agentprov telemetry ingest-falco --file falco-events.jsonl ``` `ingest-jsonl` records a telemetry batch manifest with the input file hash, mapped event IDs, event ID hash, receiver summary, and row-level mapping results. By default it also evaluates runtime policy for ingested events, so metadata-IP, private-CIDR, and secret-path rows become `policy_decisions`, `risk_signals`, `response_actions`, graph edges, and timeline rows. Use `--no-policy` when the receiver should only normalize and store telemetry. This gives the DAG an audit handle for external Falco/Tetragon/LoongCollector evidence without turning AgentProvenance into a long-term log store. The `native` format is the receiver for AgentProvenance's own eBPF sensor (`cmd/agentprov-sensor`, `source="agentprov_ebpf"`), and is auto-detected. This closes the consume-only gap: the sensor's normalized kernel events (execve, network connect classified into `metadata_ip`/`private_cidr`, file writes with their real absolute host paths) flow through the identical correlation, policy, risk, and unified-signal path as third-party telemetry. Raw file telemetry now accepts absolute host paths (e.g. a write to `/home/agent/.aws/credentials`), which the policy path rules still catch; only the workspace file-node graph keeps its relative-path constraint. `scripts/accept_native_sensor_risk.sh` proves the loop end to end (own kernel telemetry to a unified `security` signal). `ingest-falco` is the Falco-compatible receiver path. It reads Falco JSON/stdout from a file or stdin stream, maps recognized `execve`, `open/openat`, and `connect` events into normalized runtime events, correlates them by PID/container/cgroup/time evidence, and then evaluates policy by default. Raw Falco rows do not need `tool_call_id`; ToolCallScope is recovered from the binding table when possible. ## Relationship To Existing Systems AgentProvenance is designed to coexist with system-level observability projects, LLM tracing systems, and sandbox runtimes. | System category | What it owns | How AgentProvenance differs | |---|---|---| | system observability | low-intrusion system-side capture, eBPF/runtime event collection, cross-process visibility | AgentProvenance treats those events as evidence input, then builds a Git-like causality/provenance DAG, diff/blame, taint lineage, risk decision, forensics, and response-control surface | | OpenTelemetry / LLM trace platforms | spans, logs, metrics, LLM/tool traces, dashboards, latency/cost views | AgentProvenance focuses on state provenance, artifact lineage, sandbox runtime effects, security decisions, replay, and audit manifests | | HIDS / EDR / runtime security | host/process/file/network detection and enforcement | AgentProvenance adds agent context: run/session/attempt/tool_call, snapshot lineage, file diffs, artifact provenance, risk signals, baseline deviations, and response gates | | Sandbox runtimes | isolation, process/container/VM execution, filesystem and network boundaries | AgentProvenance consumes sandbox identity and telemetry; it does not try to replace Docker, OpenSandbox, gVisor, Firecracker, Kata, or Kubernetes | So the differentiation is not "another zero-SDK eBPF observer." The narrow primitive is: ```text system-side telemetry + application-side agent context -> evidence DAG -> security analysis and risk judgment -> automated response and audit trail ``` ## Quickstart Prerequisites: - Go 1.23+ - Docker Desktop or a compatible Docker daemon ```sh git clone https://github.com/ByteYellow/AgentProvenance cd AgentProvenance go build ./cmd/agentprov mkdir -p /tmp/agentprov-record-demo printf 'value = 1\n' > /tmp/agentprov-record-demo/app.py ./agentprov record --run run-record-demo --workdir /tmp/agentprov-record-demo -- \ sh -lc 'printf "value = 2\n" > app.py && echo artifact > artifact.txt' ./agentprov observe summary --run run-record-demo ./agentprov graph explain --run run-record-demo --file app.py ./agentprov adapter list ./agentprov adapter inspect filtered-jsonl --json ./scripts/demo_telemetry_jsonl.sh ./agentprov telemetry batches --run run-telemetry-jsonl-demo ./agentprov timeline --run run-telemetry-jsonl-demo ./agentprov timeline --run run-telemetry-jsonl-demo --view causality ./agentprov timeline --run run-telemetry-jsonl-demo --json ./scripts/accept_phase1.sh ``` The quick path builds `agentprov`, records a command, explains the changed file, ingests filtered substrate telemetry, and runs the Phase 1 acceptance gate. `observe summary` is the run-level observability entry point: it summarizes application context, runtime telemetry coverage, risk, baseline, response, and top evidence refs before you drill into timeline or graph queries. `demo_telemetry_jsonl.sh` is the minimal substrate telemetry path. It binds a ToolCallScope, ingests Tetragon/Falco/LoongCollector fixture JSONL from `examples/telemetry/`, lists normalized events, and explains how one substrate event entered the DAG. `accept_phase1.sh` is the machine-checkable gate for the current MVP. `timeline` is the execution timeline surface. It merges application context, runtime telemetry, evidence, policy decisions, risk signals, baseline deviations, response actions, and external effects into one time-ordered view. `--view causality` groups rows into agent context, runtime process, runtime telemetry, evidence, risk/policy, and external-effect lanes, with correlation status and drill-down commands. The JSON output is designed to feed a future UI. ## Deployment Modes AgentProvenance is intentionally usable in three deployment shapes. RL, benchmark, and evaluator users should start with the first shape; enterprise security and audit users can move toward the later shapes when they need shared ingest, retention, and query services. <p align="center"> <img src="docs/assets/deployment-modes.svg" alt="AgentProvenance deployment modes" width="920"> </p> | Mode | Shape | Best for | Tradeoff | |---|---|---|---| | Library / CLI-only recorder | one `agentprov` binary, optional Python helper, local SQLite/object store | RL rollout, evaluator jobs, benchmarks, CI, local red-team harnesses | easiest to adopt; weaker shared query and long-running ingest | | Sidecar / local daemon | `agentprov daemon serve` beside one worker or sandbox host; CLI/SDK acts as client | sandbox worker, CI runner, local security harness, medium-volume telemetry ingest | adds a local service boundary, spool, backpressure, and stable query API | | Central evidence service | shared ingest/query service with object storage, retention, auth, and UI/API | enterprise security, audit, SRE, compliance, incident review | highest operational cost; not the default RL entry point | For RL and evaluator pipelines, the default contract is lightweight and offline-first: - Install: one Go binary plus an optional thin Python package. - Call: wrap an existing command first; SDK/framework integration is optional. - Batch: every trajectory gets stable `run_id` / evidence manifest / signal context output, and query surfaces are paged. - Overhead: default capture focuses on process/file/diff/artifact/exit/resource evidence; heavier Falco/eBPF-style telemetry is an opt-in substrate. - Ownership: AgentProvenance emits evidence, deviation, risk, and trajectory signals. The RL system owns reward, ranking, dataset policy, and winner selection. - Policy: RL mode does not require online deny/kill/quarantine. Those actions are opt-in security controls; offline scoring can run later over captured EvalContext JSONL. Python usage stays thin and CLI-backed. For Deploy 1, the intended RL/evaluator entry point is one function that runs the local offline loop end to end: ```python from agentprov import Registry, Signal, run_batch_pipeline registry = Registry(name="rl-reward-signals") @registry.rule("file_change_reward") def file_change_reward(ctx): return Signal.reward_feature( "file_change_reward", float(len(ctx.file_changes())), "reward feature from file state changes", ) @registry.rule("metadata_penalty") def metadata_penalty(ctx): if ctx.has_event_type("metadata_ip"): return Signal.penalty("metadata_ip", -1.0, "metadata service access") return None result = run_batch_pipeline( [ {"run_id": "traj-0001", "workdir": "/tmp/job1", "command": ["pytest", "-q"]}, {"run_id": "traj-0002", "workdir": "/tmp/job2", "command": ["pytest", "-q"]}, ], registry, binary="./agentprov", data_dir=".agentprov-rl", engine="rl-reward-signals", import_signals=True, include_forensics=True, ) print(result.batch_id, result.signal_count) ``` For RL users, custom "rules" are ordinary Python evaluator functions. They run offline over evidence; Go keeps ownership of capture, correlation, manifests, and query integrity. The same workflow can be split into lower-level calls when the pipeline already owns scheduling or sharding: ```python from agentprov import Client, evaluate_batch client = Client(binary="./agentprov", data_dir=".agentprov-batch") batch = client.record_batch( [ {"run_id": "traj-0001", "workdir": "/tmp/job1", "command": ["pytest", "-q"]}, {"run_id": "traj-0002", "workdir": "/tmp/job2", "command": ["pytest", "-q"]}, ], ) contexts = client.batch_eval_contexts(batch_id=batch["batch_id"]) reports = evaluate_batch(contexts, registry=registry) client.import_signal_reports(reports, engine=registry.name) ``` Later, the same local store can be queried by batch, shard, job, or run: ```sh ./agentprov evidence batch-summary --latest --json ./agentprov evidence batch-summary --shard shard-0 --json ./agentprov evidence batch-summary --run traj-0001 --json ./agentprov signal batch-context --shard shard-0 --latest > eval-contexts.jsonl ./agentprov forensics export-batch --latest --json ``` ### Falco-compatible Receiver The dedicated Falco receiver is useful when Falco is already filtering kernel or runtime events on the host: ```sh ./agentprov telemetry bind --run run-falco-demo --session session-falco-demo \ --attempt attempt-falco-demo --tool-call tool-falco-demo \ --process process-falco-demo --container-id container-falco-demo --pid 4242 \ --started-at 2026-01-01T00:00:00Z ./agentprov telemetry ingest-falco \ --file examples/telemetry/falco-risk-events.jsonl --json ./agentprov telemetry list --run run-falco-demo ./agentprov telemetry list --run run-falco-demo --limit 100 --json ./agentprov telemetry list --run run-falco-demo --limit 100 --cursor <next_cursor> --json ./agentprov timeline --run run-falco-demo ./agentprov security risks --run run-falco-demo --json ./agentprov security responses --run run-falco-demo --json ``` For a live stream, pipe Falco JSON output directly: ```sh sudo falco -o json_output=true -o json_include_output_property=true | \ ./agentprov telemetry ingest-falco --file - ``` The receiver maps Falco process, file, and network rows into normalized runtime events. Metadata IP, private CIDR, and secret-path rows are promoted into security evidence: `RiskSignal`, `ResponseAction`, policy graph edges, and timeline entries. `graph explain --risk <policy_decision_id> --json` links the risk back to the raw runtime event and forward to the response action. Falco remains the substrate collector; AgentProvenance owns correlation, causality, provenance, and risk/audit linkage. The main smoke path is telemetry correlation and graph explanation: ```sh ./scripts/demo_telemetry_jsonl.sh ``` It binds a ToolCallScope, ingests raw Falco/Tetragon/LoongCollector-style runtime events that do not carry `tool_call_id`, normalizes them into the event store, correlates them back to application context, and explains the resulting causal graph. ## Security Evidence Commands ```sh ./agentprov observe summary --run <run_id> ./agentprov observe summary --run <run_id> --json ./agentprov observe coverage --run <run_id> ./agentprov observe coverage --run <run_id> --json ./agentprov observe scopes --run <run_id> ./agentprov observe scopes --run <run_id> --json ./agentprov observe event --run <run_id> --event <event_id> ./agentprov observe event --run <run_id> --event <event_id> --json ./agentprov observe process --run <run_id> --process <process_id> ./agentprov observe process --run <run_id> --process <process_id> --json ./agentprov observe flow --run <run_id> ./agentprov observe flow --run <run_id> --json ./agentprov evidence manifest --run <run_id> ./agentprov evidence manifest --run <run_id> --json ./agentprov evidence manifest --run <run_id> --materialize --json ./agentprov telemetry correlations --run <run_id> ./agentprov telemetry correlations --run <run_id> --json ./agentprov telemetry correlations --event <event_id> --json ./agentprov timeline --run <run_id> ./agentprov timeline --run <run_id> --view causality ./agentprov timeline --run <run_id> --limit 100 --cursor <next_cursor> --json ./agentprov timeline --run <run_id> --tool-call <tool_call_id> --json ./agentprov timeline --run <run_id> --process <process_id> --json ./agentprov timeline --run <run_id> --type risk_signal --json ./agentprov security risks --run <run_id> ./agentprov security risks --run <run_id> --json ./agentprov security deviations --run <run_id> ./agentprov security deviations --run <run_id> --json ./agentprov security responses --run <run_id> ./agentprov security responses --run <run_id> --json ./agentprov baseline learn --template <template_name> --run <run_id> ./agentprov baseline check --template <template_name> --run <run_id> ./agentprov signal context --run <run_id> ./agentprov signal batch-context --batch <batch_id> ./agentprov signal batch-context --shard <shard_id> ./agentprov signal batch-context --runs runs.jsonl ./agentprov signal run --run <run_id> ./agentprov signal run --run <run_id> --json ./agentprov signal run --run <run_id> \ --external "PYTHONPATH=python python3 examples/evaluators/python_signal_eval.py" --json ./agentprov signal import --run <run_id> --file external-signals.json --json ./agentprov signal import-batch --file signal-reports.jsonl --engine python-sdk --json ./agentprov compliance frameworks ./agentprov compliance map --framework owasp-asi --run <run_id> ./agentprov compliance explain --framework owasp-asi --run <run_id> --item ASI05 ./agentprov compliance gaps --framework owasp-asi --run <run_id> ./agentprov compliance report --framework nist-rfi-2026-00206 --run <run_id> ./agentprov ai tools --provider openai ./agentprov ai tools --provider anthropic ./agentprov ai call evaluate_action --input '{"event_type":"network_connect","dst_ip":"169.254.169.254"}' ./agentprov policy test examples/events/metadata-egress.jsonl ./agentprov policy decisions --run <run_id> ./agentprov forensics export <run_id> ./agentprov forensics export-batch --batch <batch_id> ./agentprov forensics export-batch --latest --include-eval-contexts --json ``` These commands are now part of the mainline security evidence surface: | Command | Purpose | |---|---| | `observe summary` | Show run-level observability coverage across application context, runtime telemetry, risks, baselines, responses, and evidence refs | | `observe coverage` | Show runtime telemetry correlation quality and list events missing session/tool_call/process identity | | `observe scopes` | Show per-tool-call observability: processes, runtime events, risks, policy decisions, responses, and drill-down links | | `observe event` | Explain one runtime event with correlated agent context, related risk/policy/response evidence, and drill-down links | | `observe process` | Explain one process with its tool_call context, runtime events, risk/policy/response evidence, and drill-down links | | `observe flow` | Show the compact causality flow from runtime events to risk signals, policy decisions, and response actions | | `evidence manifest` | Emit a run-level evidence index that binds observability summary, timeline hash, content-addressed object refs, risk/response report hashes, and recommended drill-down queries; `--materialize` writes it as an `evidence_manifest` provenance object | | `telemetry correlations` | Explain why runtime telemetry events were attached to a ToolCallScope, including raw identity, matched binding, matched keys, confidence, time window, and drill-down refs | | `timeline` | Show a time-ordered execution view across application context, runtime telemetry, evidence, risk, baseline, response, and external effects | | `security risks` | List normalized `RiskSignal` records derived from policy/runtime evidence; `--json` emits schema/hash metadata and drill-down refs to event/process/timeline/explain views | | `security deviations` | List `BaselineDeviation` records from behavior feature checks; `--json` emits schema/hash metadata and drill-down refs to timeline and summary views | | `security responses` | List recorded `ResponseAction` records such as audit, deny, kill, quarantine, taint, export, or notification hooks; `--json` emits schema/hash metadata and drill-down refs back to risk/process/explain views | | `baseline learn/check` | Learn process/file/network/risk/runtime feature vectors and emit deviation records plus baseline-derived risk signals | | `signal context/batch-context/run/import/import-batch` | Export one `EvalContext` or JSONL `EvalContext` streams for a batch/shard/run list, run built-in or external evaluators, and validate imported `EvalSignal` records or JSONL `EvalReport` batches for reward shaping, dataset filtering, quality scoring, or external benchmark consumers. AgentProvenance owns the evidence protocol, not the reward policy | | `compliance frameworks/map/explain/gaps/report` | Map run evidence to OWASP Agentic Security and NIST AI agent security assessment profiles as item-level self-assessment evidence and gap lists | | `ai tools/call` | Expose the read-only evidence query surface and inline policy pre-flight gate as provider tool schemas plus a local dispatcher. This is not an LLM gateway: models can query evidence and ask for a trusted policy verdict, but they cannot fabricate runtime telemetry, signatures, or provenance objects | | `policy test/decisions` | Evaluate events, persist policy decisions, and feed the risk/response graph | | `forensics export` | Export auditable evidence for a run; `--json` emits `agentprovenance.forensics_export/v1` with bundle path, sha256, size, and status | | `forensics export-batch` | Export a batch-level audit bundle for record batches; `--json` emits `agentprovenance.batch_forensics_export/v1` with batch summary, per-run forensics refs, optional EvalContext records, result/page hashes, and a sha256-verified bundle path | ## External Evaluator Protocol AgentProvenance exposes evidence to external scoring systems without owning their reward, ranking, or dataset policy. ```sh ./agentprov signal context --run <run_id> > eval-context.json ./agentprov signal run --run <run_id> \ --external "PYTHONPATH=python python3 examples/evaluators/python_signal_eval.py" \ --json ./agentprov signal import --run <run_id> --file external-signals.json --json ``` The protocol is intentionally small: - `EvalContext` contains trajectories, file changes, runtime events, risk signals, and response actions. - External evaluators read `EvalContext` from stdin and return `{ "signals": [...] }`. - `EvalSignal` can represent reward features, penalties, dataset labels, or quality signals. - `python/agentprov_eval` and the `agentprov` import alias provide a thin helper SDK with `Registry`, `@rule`, `evaluate_batch`, `reports_jsonl`, and CLI-backed capture/query helpers. They do not encode a reward function. - `signal import-batch` accepts JSONL EvalReport records so RL pipelines can import many offline signal reports without one command per run. This lets a benchmark harness, RL pipeline, red-team harness, or data filtering job decide how evidence becomes score, rejection, or review. In daemon mode, the same protocol is available through the local API: ```text GET /v1/signal/context?run=<run_id> POST /v1/signal/run POST /v1/signal/import ``` The daemon does not expose an HTTP endpoint that runs arbitrary external shell commands. A client can fetch `EvalContext`, execute its evaluator in its own process boundary, and import the resulting signals back into the daemon for validation. The CLI follows that shape when `--daemon-url` is set. ## Compliance Evidence, Not Certification AgentProvenance can map run evidence to security framework profiles such as OWASP Agentic Security and NIST AI agent security assessment questions: ```sh ./agentprov compliance frameworks ./agentprov compliance frameworks --ruleset examples/compliance/custom-ruleset.yaml ./agentprov compliance validate --ruleset examples/compliance/custom-ruleset.yaml ./agentprov compliance map --framework owasp-asi --run <run_id> ./agentprov compliance map --framework owasp-asi --run <run_id> --only ASI05,ASI10,TRACE ./agentprov compliance explain --framework owasp-asi --run <run_id> --item ASI05 ./agentprov compliance gaps --framework owasp-asi --run <run_id> ./agentprov compliance gaps --framework owasp-asi --run <run_id> --missing-only --json ./agentprov compliance map --framework enterprise-agent-review --ruleset examples/compliance/custom-ruleset.yaml --run <run_id> ./agentprov compliance report --framework nist-rfi-2026-00206 --run <run_id> --json ``` The output is an evidence-backed self-assessment. It does not certify compliance, provide legal advice, or replace qualified third-party audit. Each check item is derived from evidence already present in the run: timeline events, runtime telemetry, ToolCallScope bindings, policy decisions, risk signals, baseline deviations, response actions, forensics bundles, content-addressed provenance objects, and graph edges. Each item reports: ```text covered | partial | missing | not_applicable ``` with concrete `evidence_refs`, a gap when evidence is incomplete, and a recommended next step. This makes agent execution evidence usable for security reviews without turning AgentProvenance into a GRC platform. `compliance gaps` turns that same mapping into an actionable backlog of missing or partial evidence items for a run. Custom rulesets can add local frameworks and rules without replacing built-ins. The YAML model separates `rules`, `frameworks`, and `mappings`; mappings can also select built-in items such as `ASI05`, `ASI10`, or `TRACE` and reuse them inside an enterprise-specific review profile. JSON keeps `control_id` for compatibility and also emits `item_id` for the current terminology. ## AI-Callable Evidence Tools AgentProvenance can expose its evidence query surface as AI-callable tools for agent harnesses, evaluators, or review assistants: ```sh ./agentprov ai tools --provider generic ./agentprov ai tools --provider openai ./agentprov ai tools --provider anthropic ./agentprov ai call verify_run --input '{"run":"run-demo-bugfix"}' ./agentprov ai call list_events --input '{"run":"run-demo-bugfix","type":"execve","limit":10}' ./agentprov ai call get_timeline --input '{"run":"run-demo-bugfix","view":"causality"}' ./agentprov ai call evaluate_action --input '{"event_type":"execve","args":["python","-m","pytest","-q"]}' ./agentprov ai call evaluate_action --input '{"event_type":"network_connect","dst_ip":"169.254.169.254"}' ./agentprov ai mcp # serve the same catalog over stdio MCP (JSON-RPC 2.0) ``` The same catalog is rendered for the generic, OpenAI, and Anthropic providers, dispatched locally by `ai call`, and served over the Model Context Protocol by `ai mcp` (a stdio JSON-RPC 2.0 server, spec `2025-06-18`, so MCP clients see the identical tool set with no separate contract to drift). The catalog: | Tool | Purpose | |---|---| | `verify_run` | Verify object hashes, parent links, and policy/risk/response/signal integrity for a run | | `get_signals` | Return the unified behavior/cost/quality/security signal set for a run | | `list_risks` | Return security risk signals and recommended actions | | `list_events` | Return paged runtime telemetry events, optionally filtered by type | | `get_timeline` | Return the merged application-context and runtime-telemetry timeline | | `evaluate_action` | Run a proposed command, file action, or network action through the trusted policy engine without executing it (inline gate, no side effects) | | `bind_scope` | Register a ToolCallScope binding (app-asserted, forced `binding_source=ai_asserted`) so independent system telemetry can correlate to this tool call | | `record_tool_call` | Anchor an app-asserted tool call (`status=asserted`); does **not** execute anything | The read tools and `evaluate_action` are advertised read-only; `bind_scope` and `record_tool_call` are the context-write surface (`readOnlyHint=false` over MCP). This is not a model gateway, prompt router, or tool-execution sandbox. The model receives schemas and can query the evidence store, pre-flight an action through the trusted policy engine, and assert its own app-side context (`bind_scope` / `record_tool_call`). It can **never** write raw system telemetry, fabricate signatures, or forge provenance graph facts: context-write rows are recorded as `ai_asserted` and execute nothing, and verdicts are computed by the trusted engine, not the model. ## Web Dashboard <p align="center"> <img src="docs/assets/dashboard-causality.png" alt="AgentProvenance local evidence inspector preview with run selection, verify status, timeline, process tree, egress, risk signals, and causality DAG." width="100%"> </p> <p align="center"> <img src="docs/assets/dashboard-timeline-process-egress.png" alt="AgentProvenance local evidence inspector preview with run selection, verify status, timeline, process tree, egress, risk signals, and causality DAG." width="100%"> </p> ```sh ./agentprov dashboard serve # http://127.0.0.1:7396 ./agentprov dashboard serve --data-dir <dir> --addr 127.0.0.1:7396 ``` A local, read-only, single-page dashboard over the verifiable graph. Its JSON endpoints reuse the same internal functions as the CLI and AI tools, so the UI never drifts from the contract; the HTML/JS is embedded in the binary and loads no external assets (local-first). Panels: - **Causality DAG** (the signature view): model intent -> action -> policy -> risk -> response, rendered from the same `llm_intent_caused` / `llm_call` / policy->risk->response edges; click a node to highlight its causal lineage and inspect its full record. - **Verify + signature** status, **signals / risk** with recommended actions, a **paged timeline** (with correlation class per event), the **process tree**, and **egress** (destination IP and -- when captured -- resolved DNS hostname). - Live auto-refresh; flat event streams plus the verifiable signed DAG that a pure event viewer cannot show. ## Graph Commands ```sh ./agentprov graph trace --run run-demo-bugfix ./agentprov graph refs --run run-demo-bugfix ./agentprov graph log --run run-demo-bugfix ./agentprov graph materialize --run run-demo-bugfix ./agentprov graph objects --run run-demo-bugfix ./agentprov graph objects --run run-demo-bugfix --limit 50 --json ./agentprov graph objects --run run-demo-bugfix --limit 50 --cursor <next_cursor> --json ./agentprov graph verify --run run-demo-bugfix ./agentprov graph verify --run run-demo-bugfix --json ./agentprov graph replay --run run-demo-bugfix ./agentprov graph replay --run run-demo-bugfix --json ./agentprov graph trajectories --run run-demo-bugfix --json ./agentprov graph diff --run run-demo-bugfix --file calculator.py ./agentprov graph diff --run run-demo-bugfix --file calculator.py --json ./agentprov graph blame --run run-demo-bugfix --file calculator.py ./agentprov graph blame --run run-demo-bugfix --file calculator.py --json ./agentprov graph explain --run run-demo-bugfix --file calculator.py ./agentprov graph explain --run run-demo-bugfix --file calculator.py --json ./agentprov graph explain --run run-demo-bugfix --file calculator.py --depth 4 --limit 200 --json ./agentprov graph explain --run run-demo-bugfix --file calculator.py --depth 4 --limit 200 --cursor <next_cursor> --json ./agentprov graph explain --tool-call <tool_call_id> ./agentprov graph explain --risk <policy_decision_id> --json ``` What these mean: | Command | Purpose | |---|---| | `trace` | Show execution context, runtime causality, provenance edges, risk, and response-gate evidence | | `refs` | Emit stable Git-like references for attempts, snapshots, artifacts, and decisions | | `log` | Show chronological execution history | | `materialize` | Write content-addressed provenance objects | | `objects` | List content-addressed object refs, hashes, parent hashes, source IDs, paths, and sizes; supports `--limit` and `--cursor` | | `verify` | Check graph integrity, risk/response evidence chains, taint/response barriers, object hashes, replay generation, drain watermarks, telemetry batch hashes, and orphan lifecycle evidence for outlived zero-SDK child processes | | `replay` | Emit a plan-only reconstruction of the run | | `trajectories --json` | Emit per-attempt behavior evidence, risk/deviation context, cost, artifacts, and runtime events for external evaluators or RL reward/penalty pipelines | | `diff` | Compare file state between base and attempts | | `blame` | Attribute file state to attempt, tool call, process, strategy, command, and local candidate status | | `explain` | Explain a target by combining trace, runtime causality, diff/blame, telemetry receiver details, telemetry batch manifests, process observations, policy, object refs, risk signals, baseline deviations, and response evidence; `--json` emits `agentprovenance.explain/v1` with `upstream`, `downstream`, bounded `causality_path`, `query`, `evidence`, `objects`, `risks`, `telemetry_batches`, `process_observations`, and `replay_refs`; runtime events include receiver/source format, normalized event type, identity keys, schema status, and correlation status; use `--depth`, `--limit`, and `--cursor` to bound and page DAG traversal | ## Current Capability **Capture & ingest** | Capability | What it does | |---|---| | Zero-SDK record | `record -- <cmd>` snapshots the workdir, samples the process tree, captures file diffs + runtime evidence, no SDK | | Batch recorder | `record batch` records many jobs in parallel for RL/benchmark pipelines | | Native eBPF sensor | `agentprov-sensor` (Linux/arm64): exec+argv, connect, file write + sensitive **read** → `secret_path`, process_exit, privesc (setuid/setgid/ptrace), tamper (rename/unlink), TLS plaintext (hash + metadata), DNS — in-kernel noise filtering, validated live | | Evidence ingest | Falco / Tetragon / LoongCollector JSONL + native sensor → normalized events; schema-validated, app-context rejected in raw payloads, paged with integrity hashes | **Correlate & verify** | Capability | What it does | |---|---| | Execution context | explicit ToolCallScope binding across run / session / attempt / tool_call / process / container / cgroup / pid | | Runtime causality | native `runtime_*` graph edges (tool call, process tree, snapshot, event, file) | | Provenance DAG | `graph trace / refs / log / materialize / objects / verify / replay` over content-addressed objects | | Graph verify | checks object hashes, parent links, and the policy → risk → response → signal chain (white-box and external-telemetry runs) | | Correlation explain | `telemetry correlations` — raw identity, resolved context, matched binding, confidence, and time window per event | **Query & observe** | Capability | What it does | |---|---| | Timeline | `timeline [--view causality] [--json]` — merged app-context + system telemetry, paged with integrity metadata | | Observability | `observe summary / coverage / scopes / event / process / flow` — correlation coverage, gaps, per-scope and event→response views | | Evidence query | `graph explain` over file / artifact / process / event / tool_call / attempt / risk with bounded, paged causality paths | | Diff / blame | file-level diff and blame, joined to runtime events and content-addressed objects | | Evidence manifest | `evidence manifest` — a run-level, hash-indexed evidence index (`--materialize` to an object) | | Web dashboard | `dashboard serve` — local read-only UI: causality DAG, verify status, signals, timeline, process tree, egress | **Security & signals** | Capability | What it does | |---|---| | Policy / risk / taint | policy decisions, risk signals, quarantine, taint + descendant checks, response-gate eligibility | | Behavior baseline | `baseline learn / check` — process/file/network/resource features; deviations become risk signals | | Unified signals | one graph-attached `signals` table (behavior / cost / quality / security); security + quality are live producers | | Compliance | `compliance` maps evidence to OWASP Agentic + NIST AI profiles with coverage and gap reports | | Signed attestation | in-toto/DSSE ed25519 signing of evidence (`forensics export --sign-key`), detecting post-signing tamper | | Forensics bundle | `forensics export[-batch]` — a hashed audit bundle of the full evidence set | **Surfaces & integration** | Capability | What it does | |---|---| | CLI / JSON | every command has a stable `--json` contract with result/page integrity hashes | | Daemon API | `daemon serve` — binding, ingest, query, verify, record, forensics, signals over HTTP; optional bearer-token auth | | AI tools + MCP | the read surface, the `evaluate_action` gate, and context-write (`bind_scope` / `record_tool_call`) via `ai call` and stdio MCP (`ai mcp`) | | Evaluator / RL | `signal context / import`, trajectory manifests, and a Python SDK (offline batch + in-loop scoring) — emits evidence, not reward policy | | Substrate | Docker runtime (gVisor/Firecracker stubs); snapshot fork/resume/taint; telemetry spool, windows, retention, 100k-pressure tested | ## Core Demo Acceptance The main demo must prove: - Multiple attempts fork from the same clean snapshot. - Raw telemetry does not need `tool_call_id`. - Paged `graph objects` and `graph explain` responses expose stable `result_set_id` and per-page `page_hash` integrity metadata. - PID, cgroup, container, and time-window bindings can resolve execution context. - Native runtime causality records `tool_call -> process -> runtime_event`. - PID/PPID/TGID telemetry creates process-tree causality edges. - Runtime-observed `file_write` can appear in the same trajectory that produced a file diff. - Runtime-observed file events create `workspace_file/<path>` graph nodes and can be explained together with diff/blame. - Zero-SDK process observations expose outlived child processes and verify that orphan lifecycle evidence and policy decisions exist. - Timeline JSON shows zero-SDK `process_observed` events with pid, ppid, command, first/last seen timestamps, `outlived_root`, and scope boundary metadata. - Risk events can create taint and response records, but Phase 1 does not make final reward or winner decisions. - `graph diff` emits unified diff and JSON. - `graph blame` emits created/modified/deleted/unchanged state attribution. - `graph trajectories --json` emits a structured evidence package for external evaluators. Run: ```sh ./scripts/demo_telemetry_jsonl.sh ./scripts/demo_provenance_trace.sh ``` ## Architecture <p align="center"> <img src="docs/assets/architecture-overview.svg" alt="AgentProvenance architecture overview" width="920"> </p> ```mermaid flowchart TD Agent["Agent / Harness / Benchmark / Red-team / RL Pipeline"] --> CLI["agentprov CLI"] Agent --> SDK["Optional SDK / Tool Router"] Agent --> Recorder["Zero-SDK Recorder\nagentprov record -- <cmd>"] CLI --> Boundary SDK --> Boundary Recorder --> Boundary RuntimeTelemetry["Runtime Telemetry\nFalco / Tetragon / auditd / future eBPF"] --> Boundary SandboxIdentity["Sandbox Identity\ncontainer / cgroup / pid / cwd / time"] --> Boundary AppContext["Application Context\nrun / session / attempt / tool_call"] --> Boundary ExternalSignals["External Evaluator Signals\nreward_feature / penalty / label / quality"] --> Boundary subgraph Boundary["API / Ingest Boundary"] Daemon["Daemon API\ncontrol + query"] Validation["Validation / Normalization\nschema / identity / redaction"] Spool["Spool / Backpressure / Retention"] end Daemon --> Validation Validation --> Spool Spool --> Core subgraph Core["Observability + Provenance Core"] Correlation["ToolCallScope Correlation\npid / cgroup / container / time window"] Timeline["Execution Timeline\napplication context + runtime events"] Causality["Runtime Causality Graph\nprocess / file / network / event"] Provenance["Git-like Provenance DAG\nrefs / objects / diff / blame"] Evidence["Evidence Manifest\ncontent-addressed refs / hashes"] end Core --> Correlation Correlation --> Timeline Timeline --> Causality Causality --> Provenance Provenance --> Evidence Evidence --> Query["Evidence Query Surface\nobserve / timeline / explain / verify / audit"] Evidence --> Security["Security Analysis\nbaseline / policy / risk / response / forensics"] ``` Capability gating is a hard design rule. Upper layers must query the runtime, snapshot, telemetry, and isolation capabilities before assuming fast fork, memory snapshot, restore, identity, or enforcement semantics. Docker-only execution degrades to directory/filesystem provenance instead of pretending to provide VM-level resume. All producers enter through the API/Ingest Boundary. Zero-SDK recorders, SDK/tool routers, telemetry receivers, sandbox adapters, and external evaluator signals are producer inputs; they should not bypass validation, normalization, identity binding, redaction, spool/backpressure, or retention controls to write directly into the core evidence graph. ## Substrate Signals Substrate integrations are downstream of the provenance model: - Docker is the active local runtime. - OpenSandbox, gVisor, Firecracker, and Kata are future runtime substrates. - Kubernetes, Ray, Batch, and cloud systems are orchestration substrates. - Falco, Tetragon, LoongCollector, auditd, and eBPF are telemetry substrates. The current MVP consumes already-filtered Tetragon/Falco/LoongCollector JSONL through `agentprov telemetry ingest-jsonl`, adds a dedicated `agentprov telemetry ingest-falco` receiver for Falco JSON/stdout streams, records a hashable batch manifest, and does not run kernel probes. - A future `agentprov-sensor` should be capability-gated: prefer modern eBPF where the kernel and permissions support it, fall back to a legacy eBPF path where appropriate, and only use a kmod-style collector when an operator explicitly accepts that deployment model. The control plane must treat sensor capability as data, not as a hidden assumption. The project value is not collecting more logs. The value is correlating substrate signals with execution context and making them affect diff, blame, taint, replay, and auditability. ## Boundaries These boundaries are intentional: - AgentProvenance does not implement a general sandbox runtime. - It does not replace Kubernetes, Ray, OpenSandbox, Firecracker, gVisor, Kata, Falco, Tetragon, LoongCollector, or eBPF. - It is not a LangSmith clone, LLM gateway, or general observability dashboard. - It does not promise memory snapshot or VM-level instant clone in Phase 1. - It does not perform arbitrary branch auto-merge. - It does not roll back real external side effects. External actions are recorded, gated, and optionally linked to compensation hooks. - It does not make final reward, penalty, or winner decisions for RL pipelines; it emits the behavior evidence and deviation signals those systems can score. See [docs/product.md](docs/product.md) for the product direction and [docs/deployment-modes.md](docs/deployment-modes.md) for deployment shapes. [docs/comparisons.md](docs/comparisons.md) covers adjacent-system boundaries. ## Repository Layout ```text cmd/agentprov/ CLI entrypoint cmd/agentprov-sensor/ native eBPF sensor (Linux) internal/cli/ command parsing and output internal/record/ zero-SDK command recorder internal/sensor/ native eBPF sensor (exec/connect/file/privesc/tamper/TLS/DNS); Linux-only, arm64 internal/telemetry/ normalized runtime event schema, JSONL ingest, TLS HTTP metadata, correlation inputs internal/correlation/ ToolCallScope and runtime identity binding internal/provenance/ timeline, graph trace, refs, objects, diff, blame, verify, replay internal/evidence/ compact evidence records and external effects internal/security/ policy decisions, risk signals, baseline deviations, response actions internal/signals/ unified graph-attached signal model (behavior/cost/quality/security) internal/baseline/ behavior baseline learning and deviation records internal/attest/ in-toto/DSSE ed25519 evidence signing (tamper-evidence) internal/forensics/ evidence bundle export (optional signed attestation) internal/aitools/ AI-callable tool catalog (read surface + inline gate + context-write) internal/mcpserver/ stdio MCP (JSON-RPC 2.0) server over the aitools catalog internal/dashboard/ local read-only web dashboard (embedded UI) internal/substrate/ Docker/runtime/snapshot adapters used as execution substrates internal/control/ local lease/session control for substrate-backed demos internal/computerapi/ file/tool API over local sandbox sessions internal/ports/ local preview proxy support internal/stressdemo/ branch-heavy fanout demos that exercise provenance under load internal/experimental/ resource windows, scheduler, node metadata, warm-pool experiments internal/store/ SQLite schema and repositories examples/ tasks, events, policies scripts/ runnable demos docs/ product direction, MVP details, comparisons ``` The main product path lives in `record`, `telemetry`, `correlation`, `provenance`, `evidence`, `security`, `signals`, `baseline`, `attest`, and `forensics`. `substrate` contains runtime facts AgentProvenance can consume. `stressdemo` and `experimental` are intentionally separated so branch fanout and old resource experiments do not define the project identity. ## Roadmap | Phase | Goal | Main deliverables | |---|---|---| | Phase 1 | Provenance Correlation MVP | ToolCallScope, raw telemetry correlation, runtime causality DAG, diff/blame, risk/deviation records, response-gate evidence, replay and trajectory manifests | | Phase 2 | Evidence / Causality Hardening | execution timeline JSON, stable explain JSON, content-addressed objects, object parent hashes, graph verification, bounded traversal, pagination, integrity metadata | | Phase 3 | Zero-SDK Recorder Hardening | process-tree capture, delayed child process handling, cwd/time/file-diff inference, orphan lifecycle evidence, low-intrusion record mode | | Phase 4 | Real Telemetry Integration | Falco/Tetragon/LoongCollector/auditd/eBPF receivers, cgroup/container/pid correlation, kernel-side filtering assumptions | | Phase 5 | Risk / Policy / Control | configurable risk signals, behavior baselines, compliance evidence mapping, response adapters, taint propagation, quarantine, response blocking, forensics export, Feishu/DingTalk/webhook hooks, isolation escalation hooks | | Phase 6 | Scale / UI / Productization | async evidence writer, retention, content-addressed storage, snapshot GC, resource windows, high-concurrency ingest/query tests, evaluator SDK hardening, central evidence service, usable UI/API | Phases 1–4 are built and machine-checked by acceptance scripts; Phase 5 is built except the notification hooks; Phase 6 is largely landed (web dashboard, retention, content-addressed storage, concurrency, evaluator SDK), with the central evidence service deferred to v2. Recently landed: - **Unified signal model** (`internal/signals`, `agentprovenance.signals/v1`) - one graph-attached row type for behavior/cost/quality/security, replacing the per-dimension silos; security and quality are live producers, with idempotent backfill from legacy tables. - **Signed evidence attestation** (`internal/attest`) - in-toto/DSSE ed25519 signing of evidence digests, wired into `forensics export`, giving post-compromise tamper-evidence rather than integrity-only hash recompute. - **Concurrency correctness** - SQLite pragmas (incl. `busy_timeout`) applied via DSN to every pooled connection, fixing silent `SQLITE_BUSY` under concurrent writers; correlation bindings bound stale-open over-matching. - **Native eBPF sensor expansion** (`internal/sensor`, validated live on an arm64 VM) - sensitive file reads (-> `secret_path`), privilege changes (setuid/setgid/ptrace), file tamper (rename/unlink), TLS plaintext -> privacy- safe HTTP metadata + `llm_call` pairing, `process_exit` closing correlation windows, and DNS (getaddrinfo). In-kernel noise filtering before ring-buffer reserve removed a containerd-teardown firehose. Privilege-escalation policy rules (ptrace, setuid-to-root -> quarantine). - **AI surface** - MCP server (`ai mcp`, stdio JSON-RPC 2.0) and context-write tools (`bind_scope`, `record_tool_call`) over the same `internal/aitools` catalog, app-asserted within the trust boundary. - **Web dashboard** (`internal/dashboard`, `dashboard serve`) - local read-only view with the causality DAG as the signature panel, plus timeline, process tree, egress, signals, and verify/signature status. Next / open: - **Sensor breadth** — universal DNS (musl / raw UDP:53, or a `udp_sendmsg` kprobe; `getaddrinfo` covers glibc today), IPv6/UDP connect, and multi-arch (x86 `PT_REGS`; arm64-only today). `ptrace` is captured but not yet exercised end to end in a test. - **TLS depth** — HTTP/2 HPACK header decode and SSE reassembly (HTTP/1.1 plus h2 detection today). - **Tamper-evidence (v2)** — off-host / capture-time signing (KMS / TPM / transparency log). v1 is integrity plus optional local signing, not proof against a host-root attacker. - **Deploy 3** — central evidence service with process-level data-plane isolation and authz/scopes. - **Notifications** — Feishu / DingTalk / webhook response hooks. ## Development ```sh go test ./... ./scripts/accept_phase1.sh ./scripts/accept_zero_sdk_realistic.sh ./scripts/accept_falco_risk_realistic.sh ./scripts/accept_forensics_bundle.sh ./scripts/accept_batch_forensics.sh ./scripts/accept_evidence_query_pagination.sh ./scripts/accept_daemon_evidence_api.sh ./scripts/accept_telemetry_spool_backpressure.sh ./scripts/accept_telemetry_100k_pressure.sh ./scripts/accept_telemetry_event_windows.sh ./scripts/accept_signal_engine.sh ./scripts/accept_python_helper.sh ./scripts/accept_deploy1_batch_pipeline.sh ``` `go test ./...`, `go vet ./...`, and `gofmt -l` are the per-package gates. The acceptance scripts are the end-to-end ones: each drives a single path — zero-SDK record, Falco/native-sensor risk, forensics bundle, daemon evidence API, telemetry spool/pressure/windows, the signal engine, the Python helper, the Deploy 1 batch pipeline, and query pagination — and asserts correlated evidence, risk/response records, graph edges, and a clean `graph verify`. The eBPF sensor is validated on a Linux host (`go generate ./internal/sensor`, then run `agentprov-sensor`).