$ show Ajay150313/agentsre
agentsre is a Python library that applies Site Reliability Engineering (SRE) practices to AI agent systems. It provides reliability instrumentation for production agents: semantic validation of agent outputs, SLI metrics collection, circuit breaking, escalation baseline detection, cost tracking, and structured audit logging. The codebase is organized as a package with a core reliability module (aos_reliability.py), an orchestration subpackage for control-plane SLIs, a validation layer for agent decisions, and a cost optimizer for LLM spend. It also includes a fintech orchestration demo and documentation for running agents in payment systems. Architecturally, it is a library rather than a service: consumers import the Python API and embed checks into their agent loop. The primary data flow is agent action -> semantic validation -> reliability/risk checks -> orchestration SLI recording -> audit log. The system tracks metrics like escalation rates and decision quality, and can block or allow requests via a circuit-breaker style interface. External dependencies are minimal, with optional cost-optimizer imports guarded by try/except. Maintenance is active, with CI workflows, tests, and a changelog. The code is well-documented and the modules are mostly self-contained, making several components extractable for reuse in other agent platforms.
The LLM identified 4 files worth highlighting for reuse:
Core reliability primitives for AI agents: classifies change risk (LOW/ELEVATED), defines evaluation cases with weighted scoring, stores run history for trend analysis, and logs structured audit records for every approved/blocked action. Self-contained and could be extracted as a reliability SDK for agent governance.
Implements Agent Control Plane SLIs: collects reliability metrics, detects escalation anomalies against a 7-day baseline, and exposes circuit-breaker style allow/deny decisions per agent-task pair. Reusable for any multi-agent orchestration layer.
A2A semantic validator that checks agent outputs against schemas and confidence thresholds, catching 'wrong decision, right HTTP 200' failures. The validate() method is decoupled from specific agents and can be reused to gate any LLM output.
Model cost estimation with a maintainable pricing table for major models. Tracks per-model token costs and can be used to forecast and cap agent spend. The pricing table is isolated and easy to update.