EQUIPA report verification

Type: agentic-system-analyses/types/agentic-system-verification.md

Verification

The reports support a bounded implementation account, with the limits below. I read all five reports, their types, the boundary, reconciliation, empty correction answers and structural findings. The structural check reports none; there is no structural failure to route. I reviewed every declaration, its source anchors, evidence/status distinctions, annotations, explicit containment and supersession. Source inspection, rather than the analysts' validation statements, supplies the semantic checks below. No target code, tests, services or models were executed.

The frozen checkout's HEAD equals the boundary revision and its working tree is clean. Its tracked top-level entries match the coverage inventory. Source reads and searches below use SRC-1 implementation, except README claims and interactive skill instructions, which supply doctrine/design. None supplies observed operation or a causal experiment.

Runtime and workflow

I traced task execution, test-result routing and final admission separately in SRC-1, equipa/cli.py, equipa/loops.py and equipa/dispatch.py. The RT-RTE-dev-test, RT-OBJ-task-branch, RT-RTE-gated-merge and RT-BAP-merge-policy have distinct supported responsibilities. Pass-count consistency checks do not authenticate arbitrary correctness. no_tests, audit deliverables, no-change claims and clean no-ops prevent a universal independently-tested-delivery conclusion. Worktree branch validation, actual-diff classification, review provenance, pinned commits and default-branch checks qualify merge admission; they do not prevent every earlier filesystem effect. Security opt-outs and non-Git work remain outside that guarantee. The workflow account correctly distinguishes corrective tester messages from security rejection left for operator resolution, and cycle acceptance from merge-reconciled telemetry. Its annotations add handoff meaning without reallocating record identity.

I checked the alternate execution and authority surfaces against SRC-1, ollama_agent.py, equipa/agent_runner.py, equipa/isolation.py, equipa/manager.py, equipa/rlm_decompose.py, equipa/role_resolver.py and equipa/hooks/__init__.py. The RT-RTE-ollama-tools really dispatches returned names through the full handler table despite role-filtered advertisement. Its isolation refusal is separate from shell filtering. The RT-RTE-isolated-agent refuses setup failure rather than silently downgrading. Separate-user launch and checked import are not proof of deployed containment. The RT-RTE-goal-rounds, RT-RTE-rlm-review, RT-RTE-role-extension and RT-RTE-lifecycle-hooks records preserve their different owners, contexts and enforcement surfaces. In particular, an external hook's blocked mapping has force only if its caller consumes it. The RT-CLM-skill-integrity concerns manifest-listed bytes at dispatch, not universal safety or every direct call.

The two runtime model records and the MEM-CMP-embedding-service correctly leave provider parameter maintenance and exact deployed model versions uninspected. Configurable selectors do not establish immutable weights. The RT-OBJ-role-prompts and RT-BAP-role-instructions distinguish delivered instructions from compliance.

Memory and epistemic findings

I followed episode production through fallback reflection, storage, selection, final prompt construction and outcome credit in SRC-1, equipa/reflexion.py, equipa/lessons.py, equipa/prompts.py and equipa/cli.py. The episode, Q-value and embedding objects in the memory declarations, and the corresponding epistemic objects, retain different content and consumers. MEM-RTE-episode-feedback and MEM-RTE-experience-retrieval correctly qualify exposure: the tracker is task-keyed and can be overwritten; tracking precedes final trimming; uniform outcome reward does not identify individual usefulness. Ordinary telemetry does not pass configuration into episode recording. Vector scoring reranks admitted candidates rather than opening an unrestricted cross-project input route. Graph-enabled empty/error paths can skip the final base-score sort. The untyped graph identities warrant the stated caution, not a proved coherent semantic network.

I checked template extraction and SIMBA validation, insertion, before/after evaluation and pruning in SRC-1, forgesmith.py and scripts/forgesmith_simba.py. The MEM-RTE-lesson-maintenance, EPI-RTE-simba-rules and EPI-OBJ-effectiveness-score preserve structural admission, retained text, numerical evaluation and consumer-specific withdrawal. Rationale is read but omitted by the INSERT. The generated source label misses the dedicated episode-bonus helper but not the generic active lesson query. Role-level before/after arithmetic supports association only. It does not establish a rule's truth or causal contribution.

For checkpoint, soft-checkpoint, session, project-context, initiative-plan, message and persisted-output objects, I checked producers and selectors in SRC-1, equipa/checkpoints.py, equipa/sessions.py, equipa/parsing.py, equipa/tasks.py, equipa/initiative.py, equipa/messages.py and equipa/tool_result_storage.py, with prompt and loop consumers. The MEM-RTE-context-recovery correctly rejects full-conversation restoration: lexical checkpoint ordering, coexisting session/checkpoint history, the history feature gate and first-word consolidation all matter. Session state size is a target, not an invariant. The MEM-CLM-context-budget similarly distinguishes approximate trimming from enforced refusal. The offload helper does not establish an ordinary reload route. Project records and initiative summaries have implemented delivery but unresolved original authorship and governance. The interactive session-end procedure affords writes; it does not authenticate execution or universal per-item human approval. The MEM-BAP-experience-context remains historical advice at inference, unlike executable selection rules.

I checked the epistemic ledger against these transformations and consumers, including its separate checking, disposition, retention and use rows. The EPI-RTE-rubric-scores licenses calculated proxy scores, not actual code quality. SRC-1, rubric_quality_scorer.py and equipa/loops.py connect lexical/file-list scoring to storage; forgesmith.py also contains a distinct role-rubric producer and later score consumers. The report's incomplete consumer trace prevents treating its rubric account as an inventory of all measurement semantics. The EPI-OBJ-training-marker records structurally checked external metadata, not reproduced training or deployed weights. The EPI-RTE-hook-checks correctly distinguish compile/lint results from skipped checks returning zero. The EPI-RTE-context-reorder preserves section contents without demonstrating attention benefit; its numeric weight arguments are unused in the inspected body.

Improvement, reconciliation and omission stopping

I independently compared SRC-1, forgesmith.py, forgesmith_gepa.py, scripts/autoresearch_loop.py and the prompt loader. The three RT-RTE-forgesmith-changes remain materially different: manual-default append admission, versioned candidate validation/A/B selection, and direct rewrite deployment before benchmark-based keep/revert. GEPA's runtime baseline fallback is distinct from the maintenance operation marking database changes reverted. ForgeSmith can return before later maintenance phases. The public claims are correctly stronger than the source-supported existence of those mechanisms. Implemented own-organization changes and later consumers do not supply exercised dependence or demonstrated benefit. The theory-builder uncertainty is not replaced by a negative inferred from missing runtime traces.

The reconciliation correctly supersedes the three duplicate episode/lesson/Q-value object identities without transferring stronger statuses. Its differently bounded routes remain valid rather than being collapsed. The three explicit Part of: relations—memory recovery and epistemic reordering within dev/test, and build/lint within lifecycle hooks—have supported containment. No evidenced-absence records require an absence-search warrant. The one unresolved status disagreement has both findings, evidence and a prevented conclusion; its disposition is the first limit below. The remaining integration issues are refinements or unequal inspection depth, not hidden contradictions.

Omission questions were selected from the frozen tree and producer/consumer dependencies: whether alternate task drivers bypass the described product wrapper; whether memory can reach another consumer after pruning or trimming; and whether maintenance contains check/disposition routes beyond prompt mutation. Searches of initiative/manager/CLI dispatch references support shared task-admission responsibilities without proving complete scheduling coverage. MCP query inspection confirms inactive lessons remain reachable. LITM-weight and output-offload caller searches support the reports' local propagation gaps, not system-wide absence. Following ForgeSmith's full pipeline also exposed the additional GHOST finding-verification branch described in the second limit. That branch does not invalidate the reports' explicitly inspected-route conclusions about episode truth acceptance or successful self-improvement.

Further optional omission expansion stopped because these questions now have supported bounded dispositions, and no identified challenge remains that would overturn the emitted central conclusions. Optimizer internals, complete scorer/sweep semantics, plugin bodies, training consumers, cross-worktree initiative consistency and deployed state remain insufficient for exhaustive comparisons. Their presence alone does not require weakening the supported execution, retention and admission findings or pretending they were exhaustively audited.

Blockers

none

Limits

• MEM-RTE-memory-queries assigns afforded, while EPI-RTE-mcp-retrieval assigns wired to the overlapping lesson-query surface, as retained in reconciliation. SRC-1, equipa/mcp_server.py establishes registered request-to-response implementation, but not a deployed client's later incorporation. Both reports preserve those facts and leave operation uninspected. Their differently bounded route assessments do not yield one agreed status for the overlap. Withhold a single cross-report implementation-status comparison for this surface; callable retrieval, inactive-row visibility and lack of observed client reliance remain supported.
• The epistemic coverage and RT-RTE-forgesmith-changes do not give the GHOST finding-verification branch its own route analysis. Additional SRC-1 inspection of forgesmith.py finds a model scout over resolved security findings, parsed verdict/evidence, and database disposition: verification timestamps or failed-resolution status plus a follow-up task. This is a local omission from an explicitly partial maintenance inventory, not evidence of successful verification or truth acceptance. The central differentiated product/memory/self-change conclusions stand, but readers should withhold complete comparisons of security-finding rechecking, decision revision and follow-up generation. The following implementation passage establishes the omitted disposition surface, not its operation or soundness:

if verdict == "VERIFIED": conn.execute( "UPDATE decisions SET verified_at = datetime('now') WHERE id = ?", (finding["id"],), ) --- forgesmith.py