The report grouped runs by model, serving note and task version, so
runs made at different tuning were silently averaged together. Tuning
is now part of the key, and a tuning column shows how a row's settings
differ from today's defaults; it appears only when some row needs it.
tuning.executor.max_turns goes from 24 to 40. On the harder benchmark
tasks Haiku, the calibration ceiling, hit 24 on both refactor-rename
runs and both long-milestone runs, one of which had finished the work.
The cap guards against a model trying forever; it isn't a budget.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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>
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>
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>
Answering "is the workspace where the agent runs, or what it works
on?" — it is what it works on, and conflating the two is dangerous
rather than merely untidy.
The agent commits its workspace with git add -A and pushes it, so
anything inside the clone reaches the project repository. A .env
placed there would publish the LiteLLM, Matrix and Gitea tokens as a
side effect of the agent working correctly: silently, once, and
probably unnoticed. Startup now refuses both that and a state store
inside the clone, and .env.example documents the intended layout.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Nothing had ever assembled the agent. main.py was still the hello-world
placeholder from the scaffold, and no code outside the test suite
constructed an Orchestrator — which is how two fully built, fully
tested components came to be reachable from nothing: the delegation
harness, and the Gitea comment poller. Unit tests that construct a
component directly cannot catch that; only assembly can.
- config.py validates every variable before anything is built and
names all of them at once, rather than failing at first use twenty
minutes into a milestone on the call that mattered. Whitespace-only
counts as missing, since a token of spaces passes a truthiness check
and fails at the first request instead
- agent.py builds the whole graph and owns startup and shutdown order:
reconcile before accepting commands, and on stop, stop watching,
then stop talking, then close the store last
- The Gitea account is read via whoami rather than configured: it names
the identity every permission is evaluated against, and proves the
token works before anything depends on it
- Poller lifecycle is now a state-machine concern: watch the plan issue
while a gate stands, the PR while it is open, and stop on leaving.
The orchestrator owns when to watch; an injected factory owns how to
build one
- The executor reports each turn, because it runs synchronously inside
the transport callback: without it the agent is silent for a whole
milestone and a working run looks exactly like a hung one
- main.py loads .env, builds, and shuts down cleanly on SIGINT/SIGTERM
The entry-point tests changed shape: nisse-dev no longer greets, it
refuses to start unconfigured and says everything that is missing. Both
tests now run with a deliberately empty environment and a .env path
that does not exist, because the working directory's .env holds live
credentials and a test must never be one cwd away from starting a real
agent.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
dongarandClaude Opus 5.5