Releases·Tool Release
Built a verification layer for AI agents catches them lying about what they did
An independent developer launched COGEXT v2.0, an accountability layer that independently verifies whether AI agents actually completed commitments they claim, checking Gmail, GitHub, or custom webhooks before allowing a "fulfilled" state. The release targets a problem many agent builders report: agents claim actions silently fail, discovered only when clients complain.
17 engagement2 sourcesMon, Sep 14, 2026, 01:52 PM
AI Overview
- Developer releases COGEXT v2.0, which extracts agent commitments and verifies them against Gmail, GitHub, and webhooks before fulfillment is marked
- COGEXT v2.0 ships a Verifier Engine, Slack-approval Kill Switch, Contradiction Radar, Failure Predictor, and Public Audit Receipts
- COGEXT reports 8/8 E2E tests passed in production and offers a free tier via pip install cogext
- Force-fulfillment without evidence returns HTTP 409 at the DB level; stack is FastAPI, PostgreSQL, and Python
- 8 production engineers on r/AI_Agents reported building their own broken versions, prompting the unified build; free tier caps 2,500 commitments/month, no credit card
Related
Releases·Tool Release
Dev ships COGEXT, a free MIT-licensed local commitment extractor for AI agents, on PyPI
A developer released cogext-primitive, a free, MIT-licensed Python tool that extracts commitments from agent output, parses deadlines, and tracks them through a state machine in local SQLite. It is the offline entry point to COGEXT, an accountability layer for AI agents whose paid cloud version performs external verification via Gmail, GitHub, and webhooks. The tool is precision-tuned rather than recall-tuned, deliberately skipping vague or compound promises based on an audit of 120 agent outputs.
A developer released cogext-primitive, a free, MIT-licensed Python tool that extracts commitments from agent output, parses deadlines, and tracks them through a state machine in local SQLite. It is the offline entry point to COGEXT, an accountability layer for AI agents whose paid cloud version performs external verification via Gmail, GitHub, and webhooks. The tool is precision-tuned rather than recall-tuned, deliberately skipping vague or compound promises based on an audit of 120 agent outputs.
17 engagement2 sourcesredditThu, Sep 24, 2026, 03:17 PM
Products·launched
SCC Runner — verifies AI agent claims against real system evidence
SCC Runner validates whether AI agents and automations actually completed work correctly, using a Claim → Authority → Evidence → Verdict flow. It checks the agent's claimed outcomes against real system evidence rather than trusting agent output. Aimed at people building agents, automations, and DevOps workflows.
SCC Runner validates whether AI agents and automations actually completed work correctly, using a Claim → Authority → Evidence → Verdict flow. It checks the agent's claimed outcomes against real system evidence rather than trusting agent output. Aimed at people building agents, automations, and DevOps workflows.
9 engagement1 sourceredditSun, Oct 4, 2026, 08:19 PM
Products·launched
AI agent platform that runs marketing campaigns end to end for agencies
A platform for marketing agencies where specialized AI agents handle repetitive campaign work like ad copy, scheduling, spend, SEO and reporting. Agency onboarding feeds a RAG knowledge base (KPIs, brand voice, competitors) that all agents draw from to keep copy on brand. It targets agencies that want agents to act autonomously with full logging for trust and accountability.
A platform for marketing agencies where specialized AI agents handle repetitive campaign work like ad copy, scheduling, spend, SEO and reporting. Agency onboarding feeds a RAG knowledge base (KPIs, brand voice, competitors) that all agents draw from to keep copy on brand. It targets agencies that want agents to act autonomously with full logging for trust and accountability.
2 engagement1 sourceredditSat, Oct 3, 2026, 11:19 AM
Releases·Tool Release
Developer publishes COGEXT open standard for AI agent commitment tracking; LangChain not yet compliant
A developer published an open spec, COGEXT, for tracking promises AI agents make to users (e.g. 'I'll send the report by Friday') across run boundaries. Observability tools like LangSmith show traces, tokens, and latency but not whether commitments were later kept, which the spec addresses. The author proposes a LangChain callback handler and a pip-installable compliance checker, but notes zero current adoption, including LangChain.
A developer published an open spec, COGEXT, for tracking promises AI agents make to users (e.g. 'I'll send the report by Friday') across run boundaries. Observability tools like LangSmith show traces, tokens, and latency but not whether commitments were later kept, which the spec addresses. The author proposes a LangChain callback handler and a pip-installable compliance checker, but notes zero current adoption, including LangChain.
4 engagement1 sourceredditSat, Sep 26, 2026, 10:50 AM
Products·building
COGEXT — API that extracts and tracks commitments made by AI agents
COGEXT is an API that takes text produced by AI agents, extracts promises as structured commitments with confidence scores and deadlines, and tracks each through a 12-state lifecycle with webhook events. It addresses the problem that agents hallucinate, forget, and contradict themselves, giving developers a way to hold agents accountable to what they said. Aimed at teams building agent systems.
COGEXT is an API that takes text produced by AI agents, extracts promises as structured commitments with confidence scores and deadlines, and tracks each through a 12-state lifecycle with webhook events. It addresses the problem that agents hallucinate, forget, and contradict themselves, giving developers a way to hold agents accountable to what they said. Aimed at teams building agent systems.
1 engagement1 sourceredditWed, Sep 2, 2026, 09:39 PM