About AgentWatch
Built by a developer who got tired of AI agents failing silently in production.
The Creators
Why This Exists
The monitoring gap in AI agents is well documented but unsolved.
Gartner projects 40% of enterprise AI projects will be cancelled by 2027 — specifically due to monitoring gaps and inadequate risk controls.
1 in 20 agent requests fail silently in production. The output looks correct. No error is thrown. You find out three hours later when a customer complains or a database is corrupted.
Every existing tool — Langfuse, Arize Phoenix, Datadog — is post-hoc. They log what happened after it happened.
AgentWatch was built to fix the architectural problem: an agent scoring its own reasoning is structurally biased toward overconfidence. A second independent model, with no access to the agent's reasoning trace, scores every step before the next action fires.
Pre-execution. Not post-hoc. That's the difference.
What Makes It Different
Pre-execution blocking — not logging
Every competitor logs after the fact. AgentWatch holds the action before it runs. Not an alert. A hard stop.
Independent reasoning auditor
An agent scoring its own work is structurally biased. A separate model with no stake in the outcome catches what the agent misses — before the next action fires.
Git-backed rollback
Every step is a checkpoint. Irreversible actions become reversible. One command back to any prior state.
Causal memory — not episodic storage
Not just what happened but why. A temporal knowledge graph that answers: why did we choose X last week? Full reasoning trails across sessions.
OWASP Agentic Top 10 coverage
All 10 attack vectors — prompt injection, tool abuse, goal hijacking, exfiltration — blocked pre-execution. No other tool has a complete test harness for all 10.
By The Numbers
Features built
Tests passing
Framework adapters
Safety patterns blocked
Feature domains
Open source (Apache 2.0)
How It Was Built
v0.1.0 — Initial Release
Independent reasoning auditor, safety engine, live dashboard, git-backed rollback. 4 adapters. 47 tests. Zero marketing.
Community Forms
6 contributors showed up cold. 10 forks. No announcement, no marketing. The problem resonated immediately.
v0.2.0-preview — 90 Features
10 phases. 205 tests. Causal memory graph, multi-agent DAG, OWASP scanner, EU AI Act compliance, MCP server. 107 files changed.
PyPI + Landing Page
agentwatch-ai live on PyPI. Landing page deployed. Community growing. 25+ open issues across all difficulty levels.
The Road Ahead
Production load testing at scale. Framework maintainer integrations. The open reasoning trace schema — becoming the OpenTelemetry of AI agents.
Where This Is Going
Scale
Load test at 500+ concurrent sessions. Benchmark AgentWatch overhead vs baseline. Production-battle-tested, not just preview.
Protocol
Publish ReasoningTrace v1.0 as an open standard. Get LangGraph, AutoGen, Smolagents to adopt it natively. Become the OpenTelemetry of AI agent observability.
Platform
AgentWatch Cloud — managed SaaS with team billing. Plugin marketplace. AgentWatch Intelligence — AI that watches your AI and surfaces patterns automatically.
Built by the community.
Join us on GitHub