The thresholds Part H says to tune on the real inference hardware were
hardcoded, so every adjustment would have been a PR. They now live in a
`tuning:` section of a YAML config file, which also replaces flat
environment variables as the home for structure (#32).
- The file (NISSE_CONFIG, default ./nisse-dev.yaml) carries structure
and holds no secrets: credentials are ${VAR} references resolved from
the environment, and a reference to an unset variable is an error
- The environment still wins: every existing NISSE_* variable overrides
its key, and the file is optional, so .env and CI work unchanged
- Validation stays fail-fast and reports every problem at once; unknown
keys are errors, so a misspelt knob fails at startup instead of
silently doing nothing
- The config file joins the state store and .env in the check that
refuses to start when they sit inside the workspace clone
- Tuning covers the executor's turn and failure limits, the consult
budget, the delegation caps, the command timeout and the tool output
limit, which Workspace now takes as a setting; the defaults are the
values the agent already ran with
- llm.max_output_tokens reaches both LLM clients from the config
docs/Tuning-Guide.md explains the knobs for someone learning local
model tuning: how weights, quantization and the KV cache spend VRAM,
what each knob does and what goes wrong at either extreme, and an order
to tune in. nisse-dev.example.yaml documents every key and is checked
by a test so it cannot drift.
Closes#32
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A frontier model receives a scoped sub-task, works autonomously with
full tool access, and hands back; the local loop reads the handback
note and the file state, never the reasoning.
- Branch at delegation start, and abort discards it. That required
committing each turn's work onto the branch: without commits,
deleting a branch discards nothing, so "abort discards the work"
was a comforting phrase rather than a fact. The caller's own
in-flight changes are committed to their branch first, so an abort
cannot destroy work nobody delegated
- Hard caps on turns and wall clock; a breach is a defined failure
return, and its result is not usable, so the caller gates rather
than resuming on a half-edited tree
- Every event is checkpointed as it arrives, so a crash mid-delegation
leaves the branch at a known commit and a truthful transcript
- Handback is validated locally: blank fields or a non-boolean success
flag are refused and the delegation continues rather than accepting
a useless note
Two failure modes found by running it against a real model:
- A success claim with nothing changed was accepted. The handback plus
file state is the entire interface back, so a note describing work
that does not exist is worse than no note. Success now requires a
successful mutating tool call; reporting failure still does not,
since "this is impossible" is a legitimate finding
- The delegate invented the absolute path /workspace, wrote there, ran
its tests there, and handed back truthfully — with the repo empty
and git seeing nothing. Writes outside the workspace are now refused
with a message naming the real root, and both prompts state where
the workspace is instead of leaving the model to guess. This is not
sandboxing: work outside the repo is invisible to git, so the
milestone would publish an empty PR
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- 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>
dongarandClaude Opus 5.5