Chris Olson
39a338990f
Closed-loop account-intelligence system: harvest -> relevance filter vs. Capability Library -> synthesize -> score -> deliver -> feedback. - 40-capability Broadcom + VMware library - Live Exa.ai news connector with hard free-plan request ceilings - Provider-agnostic LLM layer (Gemini/Anthropic) with offline mock fallback - LLM confidence wired into composite scoring + low-confidence floor - 7-account pilot list Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
36 lines
1.5 KiB
Markdown
36 lines
1.5 KiB
Markdown
# Prospecting Agent Loop
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A closed-loop, account-intelligence system for enterprise sales prospecting: it
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monitors a list of target accounts, harvests business signals (news/web today;
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LinkedIn, job postings, and filings to come), evaluates them against a structured
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**Capability Library** of sellable products, and routes scored, signal-grounded
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opportunity briefs to the right rep — with rep feedback flowing back into scoring.
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## Contents
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- **[`sales-prospecting-agent-loop.md`](sales-prospecting-agent-loop.md)** — the full
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design document (architecture, agent loop phases, scoring, feedback loop, roadmap).
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- **[`sales-agent/`](sales-agent/)** — the runnable MVP. See
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[`sales-agent/README.md`](sales-agent/README.md) for setup and usage.
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## Status (MVP)
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- End-to-end loop runs: harvest → normalize/dedup → relevance filter → synthesize →
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score → deliver → feedback → recalibrate.
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- **Capability Library** seeded with 40 real Broadcom + VMware capabilities.
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- **News connector**: live Exa.ai with hard request ceilings (free-plan safe).
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- **LLM reasoning**: provider-agnostic (Gemini or Anthropic), with an offline mock
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fallback so the pipeline runs without a key.
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- **Pilot accounts**: 7 real companies wired in (~200 to follow).
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Secrets live in `sales-agent/.env` (gitignored). See `sales-agent/.env.example`.
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## Quick start
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```bash
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cd sales-agent
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cp .env.example .env # add your keys (Exa, Gemini/Anthropic)
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python3 run.py loop # run the loop
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python3 run.py digest # per-rep daily digest
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```
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