Chris Olson
b4e3ed9d9b
- Relevance filter: evaluate all of an account's signals in one LLM call (keyed array, chunked at 8) instead of one call per signal. - Synthesizer: produce all of an account's briefs in one call (one object per capability) instead of one call per capability. ~2 calls/account. - llm: wrap JSONDecodeError in LLMError so callers fall back to mock instead of crashing; disable Gemini thinking (thinkingBudget=0) to stop thinking tokens truncating JSON; add 429 backoff. - accounts: add Booz Allen, Dominion Energy, Hope Gas, Markel, GW Medical Faculty Associates (verified domains; CRM tier captured in crmTier). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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. Seesales-agent/README.mdfor 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