Chris Olson
fefda7481f
Capability targeting (new feature): - loop --capability <SET|ALL> scopes which capability library the loop reasons against. data/capability_sets/<name>.json (same schema as capabilities.json) holds one-off targeted products; drop a file in and pass its name, no code changes. First set: agentminder (Broadcom AgentMinder, agentic-AI identity). - capabilities.set_active_set() swaps the active library; keyword pre-filter is bypassed for small targeted sets so every signal is evaluated. New 'capability-sets' command lists them; run banner shows the active set. LLM provider — the '429' was a disguised policy block: - Anthropic rejects Claude Code OAuth tokens on /v1/messages for non-Claude-Code traffic with a fake rate_limit_error (no anthropic-ratelimit-* headers). New 'claude-cli' provider runs inference through the sanctioned headless path (claude -p --append-system-prompt --model), now the default provider. - OAuth keychain token auto-refresh via headless claude when expired. Resilience: - STATE_DIR is overridable (macOS endpoint-security locked output/state via com.apple.macl); load_memory tolerates PermissionError; exa usage counter tolerates malformed files; run.py handles Ctrl-C cleanly (no traceback). 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