Deep Agents Evaluation Framework · article
- Source
- x.com
- Author
- Viv
- Date
Context
An 's test suite helps determine which behaviors its developers improve. In an article on Deep Agents evaluation, also published on the LangChain blog, Viv describes how the team building LangChain's Deep Agents starts with specific abilities, such as finding information across files or completing a sequence of tool calls. Tests come from observed failures, selected benchmark tasks and deliberately constructed examples.
The team checks whether an agent completes the task correctly before comparing its time and cost. It also examines execution records to distinguish a direct solution from one that takes unnecessary steps. This gives developers a way to select models and revise the surrounding software against their actual workflow, instead of treating a larger test collection or one aggregate score as sufficient evidence of improvement.
- Every eval you keep is a . When one fails you tweak the or a tool description until it passes, so each retained test applies permanent pressure on the agent's behavior in that direction.
- Keep SDK plumbing tests — prompt passthrough, interrupt config, routing — out of your capability scores. Every model passes them, so they add zero signal while inflating the suite.
- Give each eval a docstring naming the capability it measures, and trace every run to a shared project. That makes the suite self-documenting and lets anyone on the team diagnose a failure or argue an eval no longer earns its slot.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
- activation steering — Changing a model's behavior by nudging its internal activations directly at runtime — no retraining, no prompt changes.
- system prompt — The standing instructions a model receives before any user input — defining its role, rules, tools, and tone for the whole conversation.
- multi-agent — Using several AI agents on one problem — splitting work in parallel, checking each other, or filling different roles like planner and reviewer.
“More evals ≠ better agents. Instead, build targeted evals that reflect desired behaviors in production.”
“Every eval is a vector that shifts the behavior of your agentic system.”
“We dogfood our agents every day. Every error becomes an opportunity to write an eval and update our agent definition & context engineering practices.”
“Create that taxonomy by looking at what they test, not where they come from.”
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