A schedule is a time trigger, and only the first step of a pipeline is really
waiting on time — every later step waits on the previous step's result. Modeling
"watch a source -> write a spec -> implement it" as three schedules made each fire
blind: on a quiet day the coding agent still spawned, paid a full model run to find
there was nothing to do, and left an empty run in Activity.
So an agent can start the next step itself. `dispatch_agent` (a tool on the bundled
MCP server, and on the pi bridge through the same --call seam) enqueues an ordinary
spawn command in the agent's own project, tagged requested_by=agent:<id> — so a
handoff is visible in Activity with no new surface to build.
- Project-scoped by construction: project_id is read from the spawn environment and
never from the tool arguments.
- Bounded rather than gated: MAX_DISPATCH_PER_RUN counts the command rows the agent
already wrote; MAX_DISPATCH_DEPTH rides in the spawn payload and is recovered by
spawn._dispatch_depth, so a chain keeps its place across a resume and a cycle
terminates instead of fanning out.
- New scout and planner roles, with built-in skills (handler-scout, handler-planner,
handler-dispatch) carrying the judgment code can't: dedupe against a memory-note
watermark, treat "nothing new" as a complete run, and write a task the receiving
cold-start agent can act on.
- A scout ending on a clean tree skips the test gate and records the new
tests_status='skipped' (migration 0017, additive CHECK widening). The gate promises
`done` means tests passed for the work that shipped; nothing shipped.
Rejected a `condition` field on schedules: "is this paper new and does it matter
here?" is a semantic judgment, so it belongs to a model, not a scheduler column. The
scout is the condition; dispatch is how it reports true — one mechanism that covers
future pipelines too.
426 tests (14 new for dispatch, 3 for the gate exemption).
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HcbDevyMcJWE6qPA56C7mZ
pi ships no web tools and claude's WebSearch/WebFetch are Anthropic-server-side
(absent on local endpoints), so these are handler-owned: a new handler.webtool
module (httpx, already a dependency) exposed through the bridge extension the
same way as the memory tools. web_fetch is provider-free — GET, HTML stripped
to readable text, size-capped. web_search resolves SEARXNG_URL, then
BRAVE_SEARCH_API_KEY, then falls back to DuckDuckGo's HTML endpoint with zero
config. Verified live: a pi agent now advertises all 14 tools (7 built-ins +
ask_operator + 4 memory + 2 web).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KdGv3u3DfTsP1S188KDhVH
pi ships seven built-in tools but activates only read/write/edit/bash by
default. The --tools flag can't fix this (it is a strict allowlist that drops
extension tools — verified against a live endpoint), so the bridge calls
setActiveTools with everything registered at session start: all seven built-ins
plus ask_operator and the four memory tools. Verified live: the model now
receives all twelve tool definitions.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KdGv3u3DfTsP1S188KDhVH
Model backend rows gain a harness column (claude | pi). A pi-harness row runs
the agent through the pi coding agent instead of the claude binary — pi speaks
the OpenAI Completions API natively, so a bare vLLM/llama.cpp/Ollama endpoint
needs no LiteLLM/claude-code-router translation proxy, and the loop is far
lighter for slow local token throughput. The Claude subscription and existing
claude-harness backends are untouched.
Parity comes from generated per-agent artifacts under ~/.handler-pi (outside
the repo tree, so the clean-tree gate never trips): models.json + settings.json
render the row as a pi provider pinned as the default model; a bundled bridge
extension (pi_bridge.ts) adapts pi's events to the exact stdin/stdout contract
of `python -m handler.hooks` — the Stop/completion gate re-prompts pi with
blockers via a follow-up message, git push runs the test/build/approval gates
and denies on failure, questions defer through an ask_operator tool into the
normal answer/resume flow, and memory recall is injected at session start. The
memory tools are registered natively (pi has no MCP), shelling to a new
`python -m handler.mcpserver --call <tool>` seam that reuses the MCP server's
implementations. Skills reuse the same ~/.claude/skills sync (pi implements the
same SKILL.md standard) plus the repo's committed .claude/skills.
Sessions are single JSONL files pre-assigned via --session, so cross-worker
resume archives/materializes exactly like claude's; the prompt travels on stdin
(pi has no -- separator). The supervisor normalizes pi's event stream on the
fly: assistant message_end feeds last_output, the final agent_end becomes the
run result. The whole chain was validated live against pi 0.84.1 with a stub
OpenAI endpoint: memory injection, push-gate denial (including the protected-
branch approval gate), stop-gate block loop, and ask_operator pause all ran
end to end through the real hooks and DB.
Also: harness selector in the dashboard Models form, pi baked into the control
image (NodeSource 22 for pi's node >= 22.19 floor), PI_BIN override, docs in
docs/local-models.md, fake_pi fixture + 12 tests (361 total green).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KdGv3u3DfTsP1S188KDhVH
Claude