# Prospecting Agent Loop

A closed-loop, account-intelligence system for enterprise sales prospecting: it
monitors a list of target accounts, harvests business signals (news/web today;
LinkedIn, job postings, and filings to come), evaluates them against a structured
**Capability Library** of sellable products, and routes scored, signal-grounded
opportunity briefs to the right rep — with rep feedback flowing back into scoring.

## Contents

- **[`sales-prospecting-agent-loop.md`](sales-prospecting-agent-loop.md)** — the full
  design document (architecture, agent loop phases, scoring, feedback loop, roadmap).
- **[`sales-agent/`](sales-agent/)** — the runnable MVP. See
  [`sales-agent/README.md`](sales-agent/README.md) for setup and usage.

## Status (MVP)

- End-to-end loop runs: harvest → normalize/dedup → relevance filter → synthesize →
  score → deliver → feedback → recalibrate.
- **Capability Library** seeded with 40 real Broadcom + VMware capabilities.
- **News connector**: live Exa.ai with hard request ceilings (free-plan safe).
- **LLM reasoning**: provider-agnostic (Gemini or Anthropic), with an offline mock
  fallback so the pipeline runs without a key.
- **Pilot accounts**: 7 real companies wired in (~200 to follow).

Secrets live in `sales-agent/.env` (gitignored). See `sales-agent/.env.example`.

## Quick start

```bash
cd sales-agent
cp .env.example .env      # add your keys (Exa, Gemini/Anthropic)
python3 run.py loop       # run the loop
python3 run.py digest     # per-rep daily digest
```