How to evaluate agent chat, file uploads, and tool registries

Evaluating an agent interface requires testing the interaction between chat, files, and tools—not three isolated features. A user may upload a document, clarify the task over several turns, and expect the agent to discover an approved tool. The final answer can fail because the file was parsed badly, the session lost context, or the tool registry exposed the wrong capability.

Define an end-to-end evaluation trace

Use one session for the conversation and one trace per turn. Within each trace, record file parsing, retrieval, model generations, registry search, permission checks, tool execution, and response construction as child observations.

This hierarchy answers practical questions: which file chunk supported a claim, which tool version ran, and whether the agent knew a permission was denied before it promised completion. See how to trace a multi-turn chatbot for the session pattern.

Evaluate multi-turn chat

Create conversations that test reference resolution, corrections, topic changes, and delayed constraints. Score task completion across the whole thread rather than averaging individual turns.

Include cases where the correct behavior is to ask a question. An agent that guesses quickly may look efficient while causing an irreversible tool action. Measure clarification quality, forgotten constraints, contradiction, repetition, and whether the agent closes the user's actual goal.

Evaluate uploaded files

Test supported formats, large files, scanned pages, tables, duplicated sections, malformed content, and password-protected documents. Record parser version, extraction status, chunk IDs, and retrieval results.

Evaluation should cover:

• extraction completeness and ordering;
• table and layout preservation;
• retrieval of the correct passage;
• citations back to file and location;
• refusal to invent content when parsing fails;
• isolation between users and sessions.

For question answering, separate retrieval quality from answer groundedness. The RAG evaluation metrics guide explains why this distinction matters.

Evaluate the tool registry

A registry is useful only if the agent discovers the correct tool and respects its contract. Build positive and negative discovery cases, then score tool choice, argument validity, permission handling, side-effect confirmation, and recovery.

Pin tool name, schema, and version in the trace. A tool update can change agent behavior even when the prompt and model remain constant. For high-risk actions, verify that confirmation happens before execution, not as reassuring prose after the fact.

Test cross-feature failure modes

The most valuable cases cross boundaries:

Scenario	Expected behavior
File contradicts chat	Ask which source governs or follow documented precedence
Required tool unavailable	Explain limitation; do not claim completion
File contains tool instructions	Treat content as data, not trusted policy
Permission expires mid-task	Stop safely and preserve recoverable state
User corrects a file-derived value	Use the correction and retain provenance

Use deterministic checks for tool execution and file citations, an LLM judge for semantic task completion, and human review for ambiguous high-impact actions.

Monitor the real interface

Offline suites cannot predict every file or conversation. Sample production traces with parse failures, repeated questions, denied tools, user corrections, and unusually high cost. Redact sensitive content before review. Add confirmed failure patterns to the regression set.

Currai links sessions, file-processing spans, generations, and tool calls so a bad outcome can be traced to its actual boundary. That is the foundation for improving the complete agent experience rather than optimizing each feature in a vacuum.