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5 Commits
Author SHA1 Message Date
dongarandClaude Opus 5.5 b934c46714 Enforce the tool-call contract, asking servers only where it works
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The executor ended a milestone on any reply without a tool call, and
nothing held a model to the offered tools. Measured on the 4070:
tool_choice "required" is enforced by vLLM (local-fast) and Anthropic,
but qwen3-14b (local-coder) under it cannot stop and writes until the
output ceiling, a known model bug. So enforcement is asked for per
model, and the loop holds the contract itself everywhere.

- The catalog reads supports_tool_choice from LiteLLM's model_info;
  BudgetedClient sends tool_choice only to models declared true, as
  "required" on the normalized route and {"type": "any"} on the
  Anthropic passthrough, and only when tools are offered
- The executor gains an explicit finish tool, the only way to end a
  milestone. Finishing with nothing changed is refused once; a prose
  reply gets one nudge, then escalates; a call to a tool not offered
  counts toward failure_limit, like a failed command; calls after
  finish are recorded as not run
- Clients record the model that actually answered. ModelFacts.answered
  recognises a proxy fallback, which once credited Claude's answers to
  a local model that could not call tools at all
- The context probe reports a fallback instead of trusting it; rerun,
  both local models now refuse over-long prompts, as they should
- The capability matrix checks every answer came from the model asked,
  that models call only offered tools, and that a declared
  supports_tool_choice is true
- Spec A15 and the tuning guide say how enforcement is requested,
  declared and backed up

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-09-24 22:54:49 +00:00
dongarandClaude Opus 5.5 494715efa9 Fit every model request to its context window
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Every turn re-sent the whole transcript with nothing checking it against
the model's window. On a local model that fails silently: Ollama drops
the start of an over-long prompt, which is where the system prompt and
the plan are. Measured on the Jetson's llama3.2, cut at 4096 tokens with
no error.

- The model catalog reads max_input_tokens and max_output_tokens from
  LiteLLM's model_info, and startup refuses a role on a model with no
  declared window
- BudgetedClient wraps both routes. It fits the canonical events before
  rendering: older tool results cut to head and tail, then the oldest
  turns dropped with a note, keeping calls with their results; a request
  that still cannot fit fails with ContextOverflow and is never sent.
  The output ceiling is the smaller of config and the model's limit
- Tokens are estimated from characters, pessimistically, and the
  estimate is corrected from reported prompt sizes, only ever upward
- The executor takes its scope from the log: the latest plan, which is
  the approved milestone plan, goes in its system prompt, and it sees
  only events after it
- Tool output keeps its head and, mostly, its tail, so a test runner's
  failure summary survives truncation
- tuning.context.keep_recent_turns and safety_margin are new knobs
- scripts/probe_context.py checks a declared window against what the
  server does; it confirmed llama3.2 cuts at the declared 4096
- The tuning guide covers the budget, declaring a local model's limits,
  and the probe

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-09-24 00:26:30 +00:00
dongarandClaude Opus 5.5 58b439088f Pass the capability matrix's models as a pytest option
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NISSE_CAPABILITY_MODELS was neither a secret nor configuration: which
models to measure is a question for one run, so it is now an argument,
--capability-models=a,b, declared in tests/conftest.py where pytest
reads options at startup. With no models the matrix skips as before, so
CI is unchanged. The last non-secret NISSE_* variable other than
NISSE_CONFIG and NISSE_ENV is gone.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-09-23 23:32:44 +00:00
dongarandClaude Opus 5.5 15d69799bf Make the config file the only home for settings
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With no production instance yet, there is no reason to carry two ways
of configuring the agent. Every environment variable that duplicated a
config key is gone; the environment now supplies only the secrets the
file references by ${…}, plus NISSE_CONFIG and NISSE_ENV to find the
files.

- AgentConfig loads from a file (load, from_file) or a parsed tree
  (from_tree, for tests); a missing file is an error naming the example
- The from_env constructors on LiteLLMClient, AnthropicPassthroughClient,
  GiteaClient, MatrixConfig and RoleMap are removed, with the env
  reading for max output tokens; clients take their settings from config
- The live integration tests share a committed agent config,
  tests/integration/nisse-test.yaml, aimed at test resources, and one
  conftest that loads it and builds clients. NISSE_LIVE_PLANNER_MODEL
  becomes its planner role; NISSE_CAPABILITY_MODELS stays as a per-run
  switch
- A missing secret now fails the live tests instead of skipping them
- CI passes only the three tokens
- .env.example is secrets only; spec A5 says config comes from the file

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-09-23 23:25:58 +00:00
dongarandClaude Opus 5.5 df449c7e34 Move configuration to a YAML file, and make the tuning knobs configurable
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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>
2026-09-23 23:14:07 +00:00