Observability, evals, and agent infrastructure
Reliable agents don't happen by accident. Teams get there by making agent behavior visible, measurable, and repeatable—so changes to prompts, tools, and models don't quietly break production.
What this covers
End-to-end tracing that shows what the agent did, in what order, and where things went off the rails (tool failures, latency spikes, runaway cost, bad plans).
Evaluation-driven development: build datasets from real examples, run automated evals, and compare versions to catch regressions before a release ships.
Long-horizon testing that looks like real usage (browsing, multi-step workflows, UI automation), not just single-turn "is the answer correct?" checks.
Technologies we implement
LangSmith
Tracing plus evaluation workflows built for LangChain/LangGraph teams—use production traces to create datasets, then run repeatable evals and regression tests as your agent evolves.
Confident AI + DeepEval
An evals stack that pairs LLM-as-a-judge with structured metrics, making it practical to run agent evals in CI and monitor quality over time.
Arize Phoenix
Open-source observability for LLM and agent apps, with session- and step-level inspection so you can replay runs, inspect spans, and pinpoint failures quickly.
Vertex AI evaluation & monitoring
For agents running on Google Cloud, we set up Vertex's agent evaluation capabilities along with safety and monitoring controls suitable for production.
Agentic testing environments
For agents that need to navigate the web or complete multi-step tasks, we add benchmark-style harnesses to validate reliability before (and alongside) deployment.
WebArena
A realistic web environment used to test autonomous agents on end-to-end tasks with objective success criteria.
BrowserGym / AgentBench-style setups
Structured environments and benchmarks that help teams measure long-horizon behavior, compare versions, and uncover brittle tool-use patterns.
How engagements typically run
Instrumentation and trace taxonomy (what to log, how to tag runs, what to measure), plus dashboards and alerts that map back to business impact.
Evaluation design: goldens, rubrics, judge prompts, and CI gates that define what "good" means for your agent—and what must never regress.
A continuous improvement loop: turn production traces and failures into new test cases so reliability increases release over release.