Agent Reliability Platform

01 / THE PROBLEM

Most LLM agent demonstrations fail in production due to compounding probabilistic errors: unexpected tool failures, circular retry loops, non-deterministic outputs, and silent context degradation that traditional APM tools cannot detect.

02 / APPROACH & WHY I BUILT IT

Developed as an open-source evaluation and observability harness to provide software engineers with visibility into agent decision trees, token usage, tool latency, and behavioral regressions across model revisions.

Built as a modular middleware layer and dashboard. Tracing hooks wrap LLM invocations and tool calls to stream span data via asynchronous queues. An evaluation engine runs programmatic assertions (syntax correctness, schema compliance, latency budgets) alongside model-graded reliability metrics.

03 / HOW THE SYSTEM WORKS

1. 01.Captures full execution trees including system prompts, agent internal reasoning, tool calls, and inputs/outputs.
2. 02.Detects cyclic retry patterns and anomalous token spikes in real time.
3. 03.Executes regression test suites across synthetic and recorded customer workflows.
4. 04.Computes quantitative reliability scores across tool precision, ground truth alignment, and deterministic execution consistency.

04 / KEY ENGINEERING DECISIONS

• ✦Zero-overhead async span ingestion to prevent tracing overhead from skewing latency metrics.
• ✦Programmatic assertions over pure LLM-as-a-judge where possible, guaranteeing deterministic evaluation thresholds.
• ✦Pluggable evaluation metrics allowing engineering teams to define custom domain-specific invariants.