3 Commits

Include renames

Author	SHA1	Message	Date
dongarandClaude Opus 5	8cfb45aed3	Enforce the token cap, and treat a spent budget as an interruption ... Exceeding a LiteLLM spend limit takes external models away for the rest of the day, which makes three existing gaps consequential rather than untidy. - A13 calls the delegation caps hard on "turns, tokens, and wall clock", and the module docstring repeated it, but the token cap was decoration: nothing counted, and result.tokens was always zero. It is now metered from the usage the provider reports, so a confused delegate cannot spend a day's allowance. A5 keeps the *money* cap on the LiteLLM key, which is a different thing; this is a scope cap - The normalized route sent no ceiling at all, so truncation detection there had nothing to detect against. Both routes now send one - A budget refusal is recognised distinctly. It is not a rate limit: retrying cannot help, so the planner no longer burns a second call to fail identically And a delegation cut off by an exhausted budget is now "interrupted" rather than "failed", which preserves its branch and leaves its intent pending. The work on that branch may be perfectly good; discarding it because the budget ran out would throw away work for a reason that has nothing to do with the work. A3's resume rule already knows how to pick such a delegation back up. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-09-22 23:54:57 +00:00
dongarandClaude Opus 5	0fd0b2e4a5	Add executor tool loop (A12/A15) — step 5b ... - 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>	2026-09-07 16:35:39 +00:00
dongarandClaude Opus 5	cf85648719	Add LLM-backed planning agent (A4/A11/A13) — step 5a ... - 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>	2026-09-07 14:47:12 +00:00