Cost-Aware Agent Evaluation: Putting Token Spend, Latency, and Quality on the Same Dashboard
Eval scores alone mislead. Here is how we build a Pareto view across cost, latency, and quality so agent releases ship on signal, not vibes.
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From the blog
Eval scores alone mislead. Here is how we build a Pareto view across cost, latency, and quality so agent releases ship on signal, not vibes.
Memory is supposed to make agents better — but does it? Build a memory eval pipeline that measures recall, precision, contradiction rate, and the freshness/staleness tradeoff.
A 'did the agent answer correctly?' pass/fail hides broken tool calls, wasted tokens, and silent retries. Here is how to evaluate intermediate steps.
Final-answer accuracy hides broken reasoning. Build an eval pipeline that scores the reasoning trace itself — coherence, faithfulness to tools, dead-end detection.
A principal engineer's playbook for curating, versioning, and growing a golden dataset for an agent — from production trace mining to annotation queues in LangSmith.
When reasoning models actually help inside an agent loop — and when they're an expensive mistake. Architecture patterns, code, and the cost/quality tradeoffs that matter.
Stop the agent BEFORE it does the wrong thing. How to wire input and output guardrails in the OpenAI Agents SDK with cheap classifiers and an eval suite that proves they work.
How short-term (thread-scoped) and long-term (cross-thread) memory actually work in LangGraph, with code, schemas, and the eviction policies that keep cost predictable.
Use LangGraph's checkpointer to make agents resumable across crashes and human-in-the-loop pauses, then replay any checkpoint into your eval pipeline.
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