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 — the full design document (architecture, agent loop phases, scoring, feedback loop, roadmap).
• sales-agent/ — the runnable MVP. See 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

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