bd-desk

# BD Desk An end-to-end business-development pipeline built on **[Claude Managed Agents](https://platform.claude.com/docs/en/managed-agents/overview)** — lead sourcing, company research, outreach drafting, a weekly nudge, and a lightweight deployable CRM to run it from. One rule holds everywhere: **the system researches and drafts; a human always sends.** Every automated path stops at a draft, and pipeline status is never machine-written.  _{All companies shown are fictional demo data from the example brand pack.} Most Managed Agents examples demo one primitive at a time. This repo composes all of them into one working system: | Primitive | Where it's used here | |---|---| | Agents / Environments / Sessions / Events | four research agents + the session-driving scripts | | **Graded outcomes** (`user.define_outcome`) | every research run ships with a rubric; the platform grades the deliverable and sends the agent back to revise | | **Offline evals** with pinned baselines | `npm run eval` — platform grader + deterministic checks, deltas vs a pinned baseline | | **Skills** (incl. bundled executable) | output formats + sweep protocol load on demand; a bundled `validate_blocks.py` the agent runs on its own draft | | **MCP consumption** | agents call the CRM's own MCP server with an allowlisted toolset | | **Vaults** | Resend key + CRM MCP bearer injected at egress — agents never see secrets | | **Multi-agent** (`multiagent` coordinator) | the Radar delegates per-sponsor mapping to a locked sub-agent in parallel threads | | **Deployments** (cron + run-on-demand) | weekly Radar sweep + weekly digest; fire any time with `radar-fire` / `digest-fire` | | **Memory stores** | the Radar's cross-run worklist (a BFS frontier over PE sponsors) | | **Dreaming** *(research preview)* | `npm run dream` consolidates the memory store + transcripts into a cleaner store | | **Files API** | deliverables written to `/mnt/session/outputs/deliverable.md` and fetched verbatim | ## The system ``` ┌──────────────────────────────────────────────┐ │ brand/brand-pack.yaml (YOUR positioning) │ │ compiled by `npm run brand-pack` into every │ │ agent prompt + the CRM's outreach drafter │ └──────────────────────────────────────────────┘ │ weekly cron ──► OPPORTUNITY RADAR ───────┼─────────► CRM (Next.js on Vercel) (graded sweep) walks the PE-sponsor │ kanban UI · REST · MCP server graph via a Sponsor │ Vercel Blob storage Mapper sub-agent; │ ▲ │ pushes qualified targets─┘ │ │ nightly cron: over MCP │ │ finalize → research │ │ → draft → follow-ups button / CLI ─► DOSSIER / SPONSOR PROFILE ─────────────┘ │ (graded runs) researches the live web, writes ▼ deliverable.md + machine-readable outreach DRAFTS people/portcos blocks (a human sends them) weekly cron ──► WEEKLY DIGEST — reads the pipeline over MCP, emails the founder the 2–3 things to do this week (ungraded on purpose: a revision cycle could re-send the email) ``` **Four agents** (`agents/*.system.md`): the **Dossier** writes a seven-section pre-call brief; the **Sponsor Profile** maps a PE fund's portfolio (its `→ ADD` portcos auto-feed the pipeline); the **Radar** hunts your ICP as a graph walk over PE sponsors, coordinating a locked **Sponsor Mapper** sub-agent; the **Digest** nudges you weekly. Prompts stay short — identity + brand canon + pointers to **skills** (`skills/`) that carry the procedure. ## Setup You need: an Anthropic API key with Managed Agents access, a Vercel account (CRM hosting + Blob), and optionally a [Resend](https://resend.com) key for email. **Cost warning:** research sessions run Claude Opus for minutes at a time. Each dossier/sweep/eval task is a real multi-minute agentic session. ```bash npm install # 1. Your brand — the config surface for everything cp brand/brand-pack.example.yaml brand/brand-pack.yaml # gitignored; edit every field cp .env.example .env # fill in as you go # 2. The CRM (deploy first — agents need its URL + MCP endpoint) cd crm && npm install vercel link && vercel blob add # create the project + a Blob store cp .env.example .env.local # fill in; mirror to Vercel env vars vercel --prod cd .. # 3. Compile the canon, provision everything (order matters; all idempotent) npm run brand-pack -- --local npm run setup # environment + dossier agent npm run setup-sponsor # sponsor-profile agent npm run setup-radar # memory store + radar agent npm run setup-digest # digest agent (needs CRM_URL/DIGEST_TO/EMAIL_FROM in .env) npm run setup-vault # the vault (needs CRM_URL) npm run setup-mcp # MCP bearer into the vault + point radar/digest at the CRM's MCP server npm run setup-skills # upload skills/ + attach to agents npm run setup-multiagent # Sponsor Mapper sub-agent + radar coordinator roster npm run brand-pack # now push the canon to the CRM too # 4. Crons npm run deploy-radar # weekly sweep (Mondays 8am ET — edit in the script) npm run deploy-digest # weekly digest (needs RESEND_API_KEY) ``` Add the agent/environment ids from `.managed-agents.json` to the CRM's Vercel env (`DOSSIER_AGENT_ID`, `SPONSOR_AGENT_ID`, `ENVIRONMENT_ID`) and redeploy once. ## Daily use  - **CRM** — the kanban board at your Vercel URL. A Research button starts a graded dossier/sponsor session; the pipeline's nightly cron finalizes finished research, researches promising untouched targets (capped), drafts missing outreach, and surfaces due follow-ups. The grader's verdict lands on each target. - **CLI** — `npm run dossier -- "Some Company"`, `npm run radar`, `npm run radar-fire` (fires the deployment now and tails it, grading verdicts included). - **MCP** — the CRM is an MCP server (`/api/mcp`, bearer `MCP_TOKEN`). Wire it into Claude Code and drive the pipeline conversationally (`crm_list_targets`, `crm_research`, `crm_draft_outreach`, …). The agents themselves use an allowlisted subset of the same server. ## Eval-driven iteration Fill `evals/tasks.json` with 2–3 real companies from your ICP (one expected Strong, one expected Skip, one sponsor), then: ```bash npm run eval -- --baseline # pin your starting point # ...change a prompt, a skill, the canon, or the model... npm run eval # scorecard with deltas vs the PINNED baseline ``` Every task runs the real agent and is scored twice: by the platform grader against the same rubric production uses, and by deterministic checks (machine blocks parse, sources cited, banned words absent, expected fit). This is how you change prompts without vibing it. ## Hard-won platform notes (read before debugging) Things we hit building this that the docs won't tell you loudly enough: 1. **Graded sessions idle transiently.** With `user.define_outcome`, a session goes `idle` around evaluation cycles while `outcome_evaluations[].result` is still `pending`/`evaluating`. "Done" is: `terminated`, OR `idle` **and** every outcome evaluation in a terminal state (`satisfied` / `max_iterations_reached` / `failed` / `interrupted`). Naive idle-means-done grabs half-finished deliverables. 2. **Graded agents write files, not messages.** Under `define_outcome`, agents put the deliverable in `/mnt/session/outputs/` and only narrate in messages. Pin a filename in the kickoff and fetch it via `files.list({scope_id: sessionId})` + `download` (allow ~1–3s indexing lag after idle). That's what this repo does. 3. **The event stream is progress, not truth.** It drops on session reschedules (e.g. model-overload retries) while the session keeps running server-side. On stream end, poll `sessions.retrieve` to the done-condition above and rebuild from `events.list`. 4. **Deployments update in place; there is no delete.** `deployments.update` changes `initial_events` / pinned agent version / resources on the live cron — run history intact. Deployments also **auto-pause after repeated errors**; if a scheduled run goes quiet, check `deployment.status` first. 5. **`agents.update` can add but not remove a `multiagent` block** — the pre-roster version stays addressable, so provision coordinator rosters as a separate version bump. No-op updates don't bump versions. 6. **Skills:** `SKILL.md` descriptions reject XML/angle-bracket tags; `display_title` is unique org-wide (on partial-run recovery, adopt the existing id via `skills.list()`); agents pinning `version: "latest"` pick up skill edits with no agent version bump. 7. **`allow_mcp_servers` only gates `limited` networking** — unrestricted environments reach MCP servers with no environment change. 8. **Dreaming is a gated research preview** — `/v1/dreams` 404s until your org is opted in. `npm run dream` is ready for when it is. ## Repo layout ``` brand/ brand-pack.example.yaml — copy to brand-pack.yaml and make it yours agents/ system prompts (+ rubrics/ used to grade every research run) skills/ on-demand procedure: formats, sweep protocol, machine-block contracts + the validate_blocks.py the agents run on their drafts src/ provisioning + run scripts (setup*, deploy*, dossier, radar, eval, dream) evals/ tasks.json (fill with YOUR companies) + pinned baseline + runs crm/ Next.js CRM: kanban UI, REST API, MCP server, nightly pipeline, Vercel Blob storage, morning-brief email ``` ## Status Managed Agents is a beta surface (`anthropic-beta: managed-agents-2026-04-01`); the SDK is pinned and this repo reflects the API as of July 2026. Built as a series of hands-on reps against the patterns in Anthropic's [cwc-workshops](https://github.com/anthropics/cwc-workshops); this project is not affiliated with Anthropic. MIT — see [LICENSE](./LICENSE).