Table of Contents

Glass Box

Frameworks narrate their own execution. Glass Box checks the story against what actually happened.

A modern agent framework hands you a tidy account of a run — the tokens it used, the tools it called, the finish reasons, the final answer. But that account is the framework's self-report. When the framework silently retries a call, drops a tool result, under-reports tokens, or a provider safety filter fires, the self-report can quietly diverge from what the model actually saw at the chat interface. Glass Box records both boundaries and reconciles them, so you can trust the numbers you evaluate on.

The two-layer model

Glass Box captures a run at two levels (ADR-019, ADR-020):

Layer What it sees Analogy
Agent boundary What the framework reports — the AgentRunResponse, its usage, its tool accounting the framework's press release
Chat / tool boundary What the model and tools actually saw — every ChatMessage request/response, every tool invocation, real token usage, real finish reasons the raw footage

Both are recorded into a single AgentTrace (the dual-boundary schema, ADR-020) whose entries are tagged with a TraceEntryScope — AgentInvocation, ChatTurn, or ToolExecution. That one dual-boundary trace is the substrate everything else reads.

Trace Fidelity — reconciling the two accounts

Trace Fidelity answers: does the framework's story match the footage? It reconciles the agent-boundary report against chat-boundary truth and flags the divergences that matter — missing/phantom tool calls, hidden retries, argument drift, token under-reporting, and suppressed finish reasons.

  • Single agent — the trace-fidelity benchmark reconciles one agent-boundary trace against one chat-boundary trace.
  • Workflows — the workflow-trace-fidelity benchmark does it per executor: it sums each executor's framework-reported ledger and compares it to that executor's chat truth, reporting Agree / TokenMismatch / FinishMismatch / NoTruth for each. Assert it in tests with result.Should().HaveTraceFidelity(chatTraces).

Under-reporting and suppressed finishes are treated as deceptions; over-reporting and sub-2% rounding noise are not. Executors without chat truth are reported as NoTruth and excluded from the aggregate, so an untraced majority can never mask a real divergence.

What Glass Box makes evaluable — the diagnostics evaluators

Because the dual-boundary trace records what the model actually saw, a family of pure-code evaluators can read signals that were previously invisible. Run them as the glass-box-diagnostics preset:

agenteval bench agentic --preset glass-box-diagnostics --subject my-agent --trace run.trace.json
Evaluator Reads Flags
Tool Reliability tool-execution successes the least-reliable tool's success rate
Tool Error Pattern tool-execution errors a failure mode concentrated across calls
Safety Intervention per-turn content_filter finishes how often a provider guardrail fired
Truncation Detection per-turn length finishes responses cut off by the output-token cap
System Prompt Drift per-turn system prompts the system prompt silently changing mid-run
System Prompt Injection system prompt vs. a trusted baseline (or an LLM judge) injected/subverted instructions
Argument Sanitization wire-level tool arguments PII/secret content reaching the tool boundary
Token Distribution per-turn completion tokens one turn dominating the run (a formatting loop)

Each evaluator Skips (and is excluded from the composite) when no trace is attached — so the preset is only meaningful on a --trace-attached run. Attach a captured trace and the evaluators become live; System Prompt Injection is the judge-capable leaf — it uses an LLM judge when one is supplied and no trusted baseline is present, and otherwise compares against the baseline deterministically (or skips).

The runtime gate

Glass Box is not only post-hoc. The same chat-boundary interception powers a runtime gate — an injection pre-gate and a PII post-gate can inspect the real request/response at the model interface and block a turn before a bad action reaches a tool. See the Glass Box Full Stack sample (Observability group, item 1).

The workflow live-truth caveat (Path-2)

Inside a live MAF InProcessExecution workflow, executor responses are not currently routed back through the instrumented chat client, so a live run yields per-executor traces without chat-boundary ChatTurn entries. Until an upstream per-executor forwarding hook lands (tracked upstream in MAF), live workflow runs report every executor as NoTruth; real per-executor reconciliation works today for direct-agent, pre-wired, or replayed traces (the offline Real vs Framework: Workflow sample, Observability item 4, shows the shape).

Try it

  • Samples — Observability group (menu I): Glass Box Full Stack, Auto-Audit, Real vs Framework: Agent, Real vs Framework: Workflow.
  • CLI — agenteval bench trace-fidelity, agenteval bench workflow-trace-fidelity, agenteval bench agentic --preset glass-box-diagnostics --trace <file>.

See also

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