- tools.py: four flat tools (read_file, write_file, list_dir,
run_command) per A15's reduced manifest. Deliberately unsandboxed:
the VM is the trust boundary (B3) and tool use is auto-approved
inside an approved unit of work (A4), so path confinement would add
failure modes without adding safety. What is enforced is
operational — timeout with process-group kill so a backgrounded
server cannot outlive its command, output truncation so a build log
cannot eat the context window, closed stdin so prompts fail fast,
and a scrubbed environment so project code never sees the agent's
credentials
- executor.py: renders the log, calls the executor role with the
reduced manifest, dispatches tool calls, and checkpoints every
result (A13). Escalation is observable, never self-assessed (A12):
consecutive command failures and a hard turn cap. Failed reads do
not count — looking around is not failing
- Until delegation exists (step 7), escalation gates to the human
with the failures attached
- control.py: start milestone now runs the executor; a stopped
milestone gates to AWAIT_INPUT
Two bugs found by running it live against real models:
- A turn-ending gate tool (submit_plan) left a tool_call with no
tool_result, which providers reject outright, so every planning
turn after the first failed with a 400. The planner now records the
gate as the call's outcome, which is also the truthful transcript
- The renderer now emits a placeholder for any tool_call lacking a
result, so a crash between call and result (A3) cannot poison the
transcript permanently
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- planner.py: LLMPlanningAgent offers exactly two tools, submit_plan
and ask_human, so gating stays structural at the tool layer; no
proceed tool exists. System prompt requires explicit, low-inference
plans because a weaker model executes them (A13)
- Model output is appended to the canonical log as it arrives, before
being interpreted, so a crash mid-planning leaves a truthful
transcript
- A bad turn (prose instead of a tool call, malformed arguments, or a
failed call) retries once, then raises PlanningError; the
orchestrator gates to AWAIT_INPUT rather than inventing a plan
- Orchestrator: appends every operator message, plan_submitted, and
gate_opened to the transcript, so re-planning sees rejection
feedback as conversation rather than out-of-band parameters; pins
the A11 role mapping at task start
- llm.py: non-JSON or non-object tool arguments now raise LLMError
instead of escaping as JSONDecodeError — observed from llama3.2,
which emits truncated argument strings
- Live planning test is opt-in on NISSE_LIVE_PLANNER_MODEL: planning
escalates unconditionally (A12) and the local model does not hold
the tool-call contract, so it awaits a frontier route
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Step-2 PR A: the approval loop end to end with a stub agent, no LLM
and no network.
- transport.py: Transport interface per A1 (send_message,
send_approval_request, ask_question, on_message) with an
InMemoryTransport; no platform SDK outside an implementation
- gates.py: PlanRevision with monotonic rev-N tags; Gate.evaluate
enforces revision binding (stale tags refused with a notice),
operator allowlisting, and approve-word-only semantics; any other
reply is rejection feedback. Channel seam ready for Gitea/Vikunja
approval sources
- control.py: B1 state machine subset with structural gating — the
agent's only actions are SubmitPlan (ends its turn) and AskHuman;
no proceed tool exists. Gate state, revision counters, task_id,
and pending questions checkpoint through StateStore, and boot
resumes an open gate or gates to AWAIT_INPUT, never blind-resumes
- gate.open/approve/reject A9 events complete the log taxonomy
- CLAUDE.md: record the plan-first working convention
- Tests: 15 unit (gates + orchestrator flows), 2 integration
(mid-gate restart with revisions intact; unresolved intents gate
the whole boot)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>