tests/integration/test_end_to_end.py drives the whole assembled agent
(real models, the Gitea sandbox, a real build host) through a milestone
with a scripted operator, and checks what bring-up run 3 checked by hand:
a PR holding only the work, pushed with the push credential; no
credential in the clone; the planner's view carrying facts, not the
executor's turns. It cleans up after itself on the sandbox and the build
host.
scripts/e2e.sh runs it on the bring-up build VM (or --local). It has its
own marker, e2e, kept out of check.sh and CI since it spends tokens.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PPQ14L3LikyefCMFBqEbKE
execution.SSHHost.execute is now the only way the agent runs anything
(L13). Commands travel as a script on SSH's stdin, so the secrets handed
to a call are never on a command line, and every value handed out is
redacted from the output. Timeouts kill the remote process group, and
an unreachable host is reported as such, not as a failed command.
Workspace and GitRepo run on the build VM. The clone uses the
repository's SSH URL, so no credential is stored in it (#89); fetches
and pushes get the push key over stdin, in a short-lived ssh-agent,
and PRs are still opened from the agent's host with its API token.
The bench runs on the build host too.
Tests reach "the build VM" through a throwaway local sshd (nisse_dev
.testing); CI installs openssh-server. The live push test now pushes
with the sandbox's deploy key (NISSE_PUSH_KEY).
Fixes#89.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PPQ14L3LikyefCMFBqEbKE
Development Plan M0.2.
- scripts/bringup.sh: start [REF] sets the previous run aside under
/root/nisse-e2e/runs/, exports the ref under test and starts the agent
as the transient systemd unit nisse-bringup, so it outlives the shell
that started it; stop (SIGTERM, clean shutdown), status, logs, and
collect (the journal log and a readable transcript).
- nisse_dev.dump (python -m nisse_dev.dump STATE_DB [--full]): a readable
dump of a state store's state, pending intents and canonical log. Opens
the store read-only, so it is safe against a live agent.
- The agent runs as root on this development VM, as it will on its own
VM. Architecture D3/D6/D7 corrected to match the operator's intent:
installing packages and toolchains on its own VM is class 2, recorded
so the environment can be rebuilt (it was class 3 with provisioning an
open question); egress names OS package mirrors. The plan's M0.2, M4.1,
M4.2 and bring-up guards follow.
Acceptance: a smoke run started, answered status in the test room,
stopped cleanly, and collect produced the log and transcript.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PPQ14L3LikyefCMFBqEbKE
Choosing a local executor model, and tuning its knobs, needs measured
success rates across repeated runs, comparable with every model
measured before.
- Four canned milestones under nisse_dev/bench/tasks: write a function,
fix a bug a failing test exposes, thread an option through several
files, and find one failure in output far past max_output_chars. Each
is an approved plan over a seed project, judged by a hidden check the
model never sees; a reference solution proves each is solvable and
that its check tells the difference
- Runs use the agent's own client wiring, now shared as
make_llm_clients, with delegation and ask_expert off: an escalation is
a failure of the model under test
- Each run is stored as one JSON line in bench/results/, committed, with
the model's backing name and declared facts, a note on how it was
served, the task's content hash, the agent's commit and tuning, the
outcome, turns, misbehaviour counts, context trims, tokens, time and
LiteLLM's cost; a fallback answer can never count as a success
- The report rebuilds the comparison from every stored run, with Wilson
95% intervals, marking runs against older task versions
- scripts/benchmark.py runs and reports; scripts/evaluate_model.sh takes
a new model through the context probe, the capability matrix and the
benchmark in one command
- Clients keep LiteLLM's per-call cost, and BudgetedClient counts the
requests it had to trim
- The tuning guide gains "Benchmarking models"
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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>
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>
- uv workspace tying nisse-core, nisse-dev, nisse-ops together
(src layouts, workspace-sourced nisse-core dependency)
- Placeholder hello-world entry points for both agents, calling
through nisse-core to exercise the package boundary
- pytest unit tests per package; integration tests (marker-gated)
running the installed console scripts as subprocesses
- ruff lint + format, nisse_* declared first-party
- Gitea Actions workflow running lint, format check, unit and
integration tests; scripts/check.sh mirrors it locally
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
dongarandClaude Opus 5.5