Agent trace recipe for broken call chains + tpk eval harness (#17 step 4, #35)

[gangtao]

Step 4 ("D") of the #17 plan, and the eval harness #35 asked for. Does not close #17; closes the tooling half of #35.

Why

• Verify & tune agent knowledge-graph tool usage (neighbors/path_between) after prompt change (#34) #35's verification: across 13 audited chat turns the agent made search_entities 61 / read_source 31 / neighbors 1 / path_between 0. The "lean on the graph" prompt nudge (feat(agent): encourage graph traversal beyond explicit call-path questions #34) did nothing.
• Deep call-path queries (e.g. "trace HTTP insert → nativelog write") return no result — sparse C++ call graph + agent doesn't use path_between #17's measurements say more nudging cannot work: the extracted C++ call graph breaks at every virtual or pointer call (77% of member calls have a receiver graphify can't type; IInterpreter::execute has 63 overrides and 0 callers). A chain cannot be completed from edges alone — the agent has to read the code at the break, which is what a person does.

Agent — a trace recipe (agent.py, new rule 7)

For "trace / how does X reach Y" questions: (a) find both endpoints — methods are named Class::method since #88; (b) path_between; mode: "calls" → done; (c) otherwise walk neighbors(id, rels=["calls"], direction="out") a hop at a time (and "in" from the far end); (d) at a dead end, read_source the function, find the interface call (x->write(...)), look up implementations with search_entities("::write", kinds=["function"]), pick by context, continue; (e) answer with every hop marked [graph] or [code] and say what is still unconfirmed. Traces may use 18 tool calls instead of 12.

tpk eval (src/tpk/evals.py, src/tpk/eval_questions.toml)

Runs a bundled 12-question set — 3 trace, 3 relationship, 3 architecture, 3 docs/deployment — through the real agent on the live graph and records, per question: tool-call sequence, whether a graph-edge tool was used, call count vs budget, a keyword sanity check on the answer, latency, errors. Trace/relationship questions require neighbors or path_between.

tpk eval --yes -o before.json
tpk eval --yes -o after.json --compare before.json      # passed / graph_tool_rate / avg_tool_calls, fixed + regressed ids
tpk eval --only trace --limit 2                          # narrow a run;  --questions my.toml for a custom set

Output: JSON report + a markdown table by category. An agent/gateway error fails that question and is recorded; it never aborts the run. It is a behaviour probe, not a correctness benchmark — expect_answer_any only catches an answer that isn't about the thing asked. Costs real LLM tokens (confirmation prompt unless --yes). The question file ships inside the package (verified in the built wheel), so it works in the container.

Testing

• tests/test_evals.py (infra-free, fake LangGraph agent): tool-sequence/answer extraction incl. Anthropic block content and parallel tool calls; scoring (tools / answer / budget); error isolation; per-category summary; compare (metric deltas, fixed / regressed); the bundled set is well-formed; report round-trip; the CLI end-to-end with --only --limit --compare.
• tests/test_agent.py: the prompt carries the recipe.
• 58 infra-free tests pass. The DB-backed suite was not re-run; this PR touches no DB code.

Live results (2026-09-21) — local stack, both proton entries re-ingested with #88, agent = Opus 4.8

Same image, same graph, same 12 questions; only agent.py differs (main's prompt vs this PR's).

	main prompt	+ trace recipe
passed	11 / 12	11 / 12
used a graph-edge tool	67% (trace 100%, relationship 100%, architecture 67%, docs 0%)	67% (identical split)
avg tool calls	6.92	7.83
errors	0	0

Honest reading:

• The recipe does not move the numbers. One run per arm, so ±1–2 calls per question is noise, but there is no measurable gain in pass rate or graph usage, and a small cost (+0.9 calls/question).
• The big change had already happened — from ingest: derive code kinds and class-qualified names from graph structure (#17, step 1) #88 (kinds / Class::method names, after re-ingest) and path_between: directed call chains (#17, step 2) — re-land of #89 onto main #90 (path_between + its prompt text). Production chats audited before those: 1 of 13 turns touched a graph edge (~8%). Baseline here: 67%, and 100% on trace and relationship questions. "Which classes inherit from IInterpreter?" is answered in 2 calls.
• The Deep call-path queries (e.g. "trace HTTP insert → nativelog write") return no result — sparse C++ call graph + agent doesn't use path_between #17 question is now answered. "Trace the call path from HTTP insert to nativelog write" → HTTPHandler::processQuery → InterpreterInsertQuery::execute → StorageStream::write → StreamSink → shard store → NativeLog, stitched across the interface breaks by reading source. It takes 21–22 tool calls; it "failed" only the harness's 20-call budget, which was my arbitrary number — that one question now has max_tool_calls = 24 (still under the recursion limit).
• What the recipe does buy is provenance. With it, that answer marks each hop [graph] / [code] (2 / 4) and states that path_between found only a related chain, not a call chain. Without it the agent presented the whole path as "the confirmed chain". That is the reason to keep it; it is not a performance change.

So: the UI's suggested trace question can stay. Re-ingest of the proton entries took ~4 min each (app peak ≈ 1.6 GiB); node count for proton-enterprise@v3.3.1 went 163,924 → 168,419 because overloads no longer collide.

Caveats: n = 1 run per arm; the answer check is a keyword sanity check, not a correctness judgement — I read the hard-trace answers, not all 24.

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