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(Phase 6)
Two prerequisites for safe parallel evaluator fan-out (later phase):
1. Auto-cascade-fail: previously, a failed task's dependents just sat
PENDING/QUEUED forever (or until something eventually tried to dispatch
them and discovered the dependency was dead). Pool.cascadeFail now fires
right after a task lands in a terminal failure state (FAILED/TIMED_OUT/
CANCELLED/BUDGET_EXCEEDED, from handleRunResult, the budget-gate reject
path, and the checkDepsReady dependency-failure path), recursively
cancelling every not-yet-run dependent (PENDING/QUEUED only -- RUNNING and
terminal states are left alone) with a message referencing the upstream
failure. A visited-set guards recursion, which turned out to be
load-bearing rather than defense-in-depth: task creation does not prevent
dependency cycles anywhere in this codebase.
Correction to an earlier assumption: internal/executor's
waitForDependencies is dead code, never called. The live mechanism is
checkDepsReady, invoked synchronously in execute() with a self-requeue via
time.AfterFunc. Added a freshness re-check (GetTask, bail if no longer
QUEUED) at the top of that block so a task cascade-cancelled while sitting
in the requeue loop stops silently instead of hitting an invalid
CANCELLED->CANCELLED transition or, worse, still getting dispatched.
2. storeChannel.SpawnSubtask hardcoded Agent.Type: "claude" on every spawned
child regardless of what role it should play -- a hard blocker for a
Planner/Builder task spawning role-typed evaluator subtasks. SubtaskSpec
(internal/agentchannel) gains a Role field; when set, the child task gets
Agent.Role instead of a hardcoded Type, so Phase 5's role-resolution picks
provider/model from that role's escalation ladder. spec.Role == "" (every
existing caller) preserves today's exact behavior byte-for-byte -- proven
by an explicit regression test, not just new-feature coverage. Threaded
the new `role` parameter through both spawn_subtask transports: the native
tool-use loop (internal/agentloop/tools.go) and the MCP tool exposed to
ContainerRunner-driven claude/gemini agents (internal/executor/agentmcp.go).
go build/vet/test -race -count=1 all pass, full suite (20 packages).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01V1moSNCJRcP6kykA4tyUSs
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Splits LocalRunner's OpenAI-specific agentic loop into reusable, provider-
agnostic pieces so later phases can add native Anthropic/OpenAI/Google/Groq/
OpenRouter adapters without duplicating the control flow:
- internal/provider: neutral Provider/ChatRequest/ChatResponse types, plus
an openaicompat adapter wrapping the existing internal/llm.Client unchanged
- internal/sandbox: Sandbox interface + HostSandbox (git clone/push/cleanup,
read_file/write_file/run_bash/glob), lifted verbatim from local.go/localtools.go
- internal/agentloop: the extracted tool-use loop (request/response/tool-
dispatch/loop, ask_user blocking, stream-json envelope, summary fallback)
- internal/agentchannel: AgentChannel/SubtaskSpec/BlockedError/ErrAgentBlocked
moved out of internal/executor so agentloop can use them without an import
cycle; internal/executor re-exports via type aliases, so no call site changes
- internal/executor/nativerunner.go: NativeRunner replaces LocalRunner,
wiring agentloop.Loop + openaicompat + HostSandbox together
- config.Providers map[string]ProviderConfig added (unused until Phase 2+)
Zero intended behavior change: go test -race ./... passes across all
packages, and end-to-end stream-json/summary/changestats output was verified
byte-compatible against a fake OpenAI-compatible server. Adds test coverage
for sandbox tool-dispatch (git clone/push, read/write/bash/glob) that
LocalRunner never had.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01V1moSNCJRcP6kykA4tyUSs
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ContainerRunner now mints a per-task MCP token (from an injected Registry),
writes a claude mcp-config into the workspace pointing at the host agent MCP
server over host.docker.internal with that bearer, and adds --mcp-config to the
in-container claude invocation. The token is revoked when the run ends. After
the run, a buffered ask_user (PendingQuestion on the channel) is converted into
a BlockedError — the MCP path to BLOCKED — with the file-based question.json
kept as a fallback for in-flight tasks started on the old wire.
The API server mounts the StreamableHTTP MCP handler at POST/GET/DELETE /mcp
when a registry is provided; serve.go constructs one Registry shared by the
runners (mint) and the server (resolve). Minting is skipped for gemini agents.
Tests: writeMCPConfig output shape, buildInnerCmd flag presence/absence, and an
api-level end-to-end MCP tool call through NewServer/Handler proving the route
is mounted (and absent without a registry).
https://claude.ai/code/session_01SESwn7kQ7oP62trWw6pc39
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Adds the agent-facing MCP transport foundation: a Registry that mints a
per-task bearer token bound to a freshly built MCP server exposing the four
agent tools (ask_user, report_summary, spawn_subtask, record_progress), and
an HTTP handler (StreamableHTTP) that resolves the token to that server. The
server never trusts an agent-supplied task ID — context comes from the token.
The default storeChannel now buffers summary and question signals under a
mutex (an MCP tool call lands on an HTTP-handler goroutine mid-run), exposing
ReportedSummary/PendingQuestion. The pool flushes the buffered summary onto
the execution after the run, replacing the runner's direct exec.Summary write
and keeping the read race-free.
ask_user follows the record-and-resume model: it buffers the question, returns
ErrAgentBlocked, and the tool tells the agent to end its turn; the run blocks
and resumes later via claude --resume (no live slot held).
Tests cover registry lifecycle, in-memory tool dispatch, and HTTP end-to-end
with bearer auth (valid token dispatches; invalid token rejected).
Not yet wired into the runners or mounted on the API server — next increment.
https://claude.ai/code/session_01SESwn7kQ7oP62trWw6pc39
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Defines AgentChannel — the normalized interface by which a runner reports
agent-originated signals (AskUser, ReportSummary, SpawnSubtask,
RecordProgress) — plus a default storeChannel implementation backed by
storage. Runner.Run now takes an AgentChannel; the pool constructs one
per execution.
The file transport routes its post-exit summary detection through
ch.ReportSummary (buffered onto the execution so the pool still applies
its extract/synthesize fallbacks, no double-write). AskUser returns
ErrAgentBlocked since write-and-exit cannot answer in-session; question
persistence stays with the pool's BlockedError handling. SpawnSubtask
and RecordProgress are implemented and tested, ready for the MCP
transport in Phase 2 where the channel becomes fully load-bearing.
Store gains CreateEvent so the channel can emit agent_message events.
https://claude.ai/code/session_01SESwn7kQ7oP62trWw6pc39
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