Autogenesis report verification

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

Verification

The reports support a bounded source-level account. No publication blocker was found. One local return-payload qualification is retained below. The structural report-check says none; all four correction-answer inputs are empty. Neither supplies an argument that replaces independent verification.

Boundary, records and evidence strength

I rechecked the source checkout's HEAD and clean short status against boundary, enumerated its tracked root entries, and inspected implementation and shipped doctrine only within SRC-1. The enclosing-runtime classification is supported by task scheduling, runtime pumps, model invocation and effect dispatch. Shipped example drivers and Gateway clients remain inside the functional boundary. No target, test, example, provider or external service was executed. No observed-run or causal source is registered.

I reviewed all 20 runtime, 21 memory and 15 epistemic declarations, the workflow account and annotations, and reconciliation. Checks covered each record's referent, cited source passages and their surrounding behavior, representation and substrate, producer-to-consumer progression, admission, status, authority and stated uncertainty. I assessed source support rather than repeating deterministic quotation-occurrence or link checks. No evidenced-absence records are declared. The wired implementation and uninspected operation fields are appropriate; prose about installed capacity does not establish activation or benefit. The theory-builder conditions remain separate and do not acquire observed consumption, criticism or iteration from shipped instructions alone.

Runtime and delivery

For RT-RTE-task-execution, I traced Gateway startup/submission/queued execution, native capability assembly, Agent decision and concurrent action dispatch, round completion and conclusion. SRC-1: autogenesis/gateway/service.py, autogenesis/agent/native_tools.py, autogenesis/agent/types.py, autogenesis/runtime/server.py. Registry class construction and the shipped direct example support the distinction between the MetaAgent implementation and a configuration field naming the base type. The workflow's selected path and its two annotations preserve that sequence without inventing a mandatory actor/verifier pipeline.

The terminal warning is substantive and correct: MetaAgent can set run.done=True with an explicitly incomplete result; base success uses that flag and the constraint flag. Gateway publishes task.completed on normal return, not independent acceptance. The base and MetaAgent no-progress guards are different branches. SRC-1: autogenesis/agent/actor/meta_agent.py, autogenesis/agent/types.py, autogenesis/gateway/service.py.

For RT-RTE-child-delegation, protocol construction, existing-file filtering, parent-ref escalation and Agent return handling establish selected child inputs and message-only return, not whole-history inheritance or effect isolation. For RT-RTE-skill-instructions, the skill manager returns rewritten Markdown and resource lists, not an executor. Native assembly really applies the inherited worker-type filter; MetaAgent does not override it. The orchestrator-typed evolution playbook therefore cannot be assumed visible through this default projection. SRC-1: autogenesis/protocol/server.py, autogenesis/skill/context.py, autogenesis/agent/native_tools.py, autogenesis/agent/actor/meta_agent.py.

The shell, workflow, playground and configuration route records retain distinct enforcement owners. Bash checks its own registered entity and can execute locally; manager argument binding and timeouts do not create confinement. Workflow invokes managers without the Agent denylist; resume checks identity/hash and caches completed invocations, without proving exactly-once external effects. Playground supplies messages directly to the model manager. Stream retries stop after an emitted event, and provider plugin forwarding is not Agent action dispatch. Administrative updates do not inherit every evolution gate. SRC-1: autogenesis/tool/default/bash.py, autogenesis/tool/context.py, autogenesis/permission/context.py, autogenesis/workflow/runtime.py, autogenesis/model/context.py, autogenesis/model/openrouter/chat.py, autogenesis/gateway/service.py. These checks also support the model component's mutable endpoint/version qualification and the three runtime behavioral-authority paths.

Change admission and epistemic consequences

I traced RT-RTE-capability-admission through file resolution, staged validation, automatic promotion, dynamic loading, registration, archive/manifest commitment, smoke decision and recovery. The code supports both consequential doctrine mismatches: explicit user approval is not enforced on the automatic hook route, and smoke follows manifest commitment rather than preceding it. Startup loading bypasses that smoke branch. The probe selects a general agent and tests success/constraint flags, not exact OK or required invocation of the changed capability. SRC-1: autogenesis/hook/default/tool_registration.py, autogenesis/hook/promotion.py, autogenesis/sandbox/project.py, autogenesis/extension/server.py, autogenesis/extension/smoke_gate.py.

The overwrite guarantee is correctly restricted to supported lookups and paths; class loading precedes its check, prompts carry no flag, and memory lacks an explicit current-flag lookup. Memory generation has its own registration hook and does not establish evaluated content migration. Version maps, workflow checkpoints and extension archives have different consumers and recovery limits. SRC-1: autogenesis/hook/default/memory_registration.py, autogenesis/agent/generator/memory_generate_agent.py, autogenesis/command/default/checkpoint.py, autogenesis/command/default/restore_checkpoint.py, autogenesis/memory/context.py. These findings support the separate memory admission/recovery records rather than a universal rollback guarantee.

I checked all four system claims against their respective doctrine and implementation. The epistemic comparison preserves standing improvement-directed change without upgrading it to exercised or beneficial self-improvement. Neither advisory evaluation policy nor installation supplies the missing evidence-shaped update and subsequent causal dependence.

Retained material, checks and later consumers

For MEM-RTE-tier-context, I independently read event translation, tier ingestion, selection, compaction and persistence. The hook's stop-success projection, oldest-chunk removal, nonempty insertion, empty-result restoration, exception branch, bounded summaries and fresh session construction support the memory distinctions. The HTML/JSON projections and constraint renderer do not establish a separate verified-constraint store or restart prompt loader. Todo Markdown has a separate actual per-turn disk reader. SRC-1: autogenesis/hook/default/memory.py, autogenesis/hook/default/compact.py, autogenesis/memory/default/tiered.py, autogenesis/memory/default/general_memory_system.py, autogenesis/memory/default/file_system_memory.py, extension/memory/constraint_memory.py, autogenesis/tool/default/todo.py, autogenesis/agent/types.py.

I checked the diagnostic container and each declared conversation, chat, trace, snapshot and trajectory part against its own writer/read API and consumer. Transcript display, trace disk reads, RAM-addressed trajectory exports and prompt snapshot files are not interchangeable recovery routes. The corpus record and ranking route correctly distinguish document payload, backend metadata and transient statistical representations. Workflow normalization retains retrieved data, but no automatic corpus-to-Agent injection follows. SRC-1: autogenesis/conversation/server.py, autogenesis/gateway/service.py, autogenesis/trace/writer.py, autogenesis/hook/default/snapshot_hook.py, autogenesis/trajectory/server.py, autogenesis/knowledge/server.py, autogenesis/knowledge/default/rankers.py, autogenesis/workflow/runtime.py.

The epistemic objects and ledger functions preserve acquisition, transformation, checking, disposition and retention as distinct. Web summary/merge code has no fidelity check; exact match establishes only the encoded comparison; record projection can discard provenance; return arithmetic depends on supplied ordering and numeric premises. VERIFY returns verdicts without consensus aggregation. Workflow evaluation checks version/run/hash/terminality/success and duplicates; its health threshold aggregates submitted evidence rather than enforcing adoption. Inspection exposes the summary and separately carries individual records in data. SRC-1: autogenesis/tool/default/web_fetcher.py, autogenesis/tool/default/web_searcher.py, autogenesis/process/default/records.py, autogenesis/benchmark/default/exact_match.py, autogenesis/benchmark/server.py, autogenesis/workflow/context.py, autogenesis/agent/evaluator/workflow_evaluate_agent.py, autogenesis/tool/default/inspect_workflow.py. The one payload qualification is in Limits. No supported system-wide absence of acceptance, derivation or theory building is inferred.

I checked every Part of: relation. Tier context is a material leg of the task route; the corpus and ranking mechanism belong to the knowledge route; checking, process transformation and evaluation maintenance belong to the workflow lifecycle; diagnostic child objects belong to their declared container. Containment does not merge their fields or evidence strengths.

All four reconciliation amendments identify the same source-native referents and retain both declarations' additional findings. Corpus objects, compaction routes, evaluation objects and evaluation-maintenance routes are valid duplicate matches. Keeping the combined tier-history and diagnostic containers alongside their parts is justified. The integration dispositions do not confuse tool-admission thresholds with workflow health metrics, display with prompt restoration, or static skill delivery with retained action history. The approval and smoke discrepancies are source doctrine/implementation mismatches, not unresolved analyst disagreements. No unmarked material inter-report conflict or missing required part was found.

Omission questions were selected from the tracked tree and producer/consumer dependencies, not just quoted anchors. I challenged whether shipped callers supplied an overlooked restart-to-model route, whether another admission path imposed the claimed approval/quality veto, and whether the reviewer or workflow evaluator imposed a completion gate. Additional inspection included SRC-1 frontend/src/canvas/PlaygroundPanel.tsx, frontend/src/science/Conversation.tsx, autogenesis/canvas/server.py, autogenesis/agent/actor/reviewer_agent.py, and searches across autogenesis, examples and configs for trajectory readers/rewards, feedback, memory loading and workflow health consumers. Replay populates client state; the Playground keeps flow replay and direct-model histories separate. Canvas library import/export and ephemeral workflow execution are additional shipped paths, not evidence that tier files restore Agent history. Reviewer implementation uses the ordinary advisory loop; workflow evidence admission is an explicit narrow read-only exception, not a mandatory quality veto.

These searches preserve the reported positive routes and their explicit partial inventories. They do not establish universal non-use, all frontend resend behavior, all custom memory consumers or complete science/kernel coverage. Further optional expansion stopped because the selected challenges have supported dispositions, no identified material contradiction remains undisposed, and the retained conclusions make none of those universal comparisons. Remaining source availability alone does not justify an exhaustive audit.

Blockers

none

Limits

  • The immediate-return wording in MEM-RTE-evaluation-records and EPI-RTE-workflow-evidence should not be used to infer that the evolution_tool response's individual data.evaluation is the normalized stored record. SRC-1 autogenesis/workflow/context.py creates a copied record with run-derived metrics and timestamp; autogenesis/tool/default/evolution.py ignores that returned replacement and serializes its original local evaluation. The stored ledger, current-version summary and inspection consumer remain supported, so this local payload discrepancy does not undermine the lineage checks or advisory-health conclusion. Withhold equality between submitted/returned individual metrics and stored run-derived metrics without a later ledger read. The load-bearing return path is:

    workflow_manager.record_evaluation(evaluation) summary = workflow_manager.evaluation_summary(evaluation.workflow_name) return Response(type=ResponseType.TOOL, success=True, message=f"Recorded evaluation for workflow:{evaluation.workflow_name}. Summary: {summary}", data={"evaluation": evaluation.model_dump(), "summary": summary}) --- autogenesis/tool/default/evolution.py