For Claude Code teams, see primitives.md. In Codex, use the active collaboration-tool schemas and codex-quick-reference.md; do not assume Claude's team files or task store exist.

Resolve the host primitives before dispatching:

The comparison and examples immediately below describe Claude's two dispatch modes. Codex uses for both one-shot and follow-up work:

spawn_agent({ task_name: "find_auth", fork_turns: "all", message: "Find the auth entry points and return paths only." })

Never emit a tool name or argument the active harness does not expose.

Subagent (short-lived, returns result):

Task({ subagent_type: "Explore", description: "Find auth files", prompt: "..." })

Teammate (persistent, communicates via inbox):

Teammate({ operation: "spawnTeam", team_name: "my-project" })
Task({ team_name: "my-project", name: "worker", subagent_type: "general-purpose",
       prompt: "...", run_in_background: true })

For detailed agent type descriptions, see agent-types.md.

When dispatching independent read-only or worktree-isolated agents, issue the harness's native spawn calls without waiting between them. In Claude Code, place all calls in one assistant message. In Codex, issue the calls concurrently up to the active-agent limit. Waiting for one result before spawning the next serializes the fan-out.

// Correct: one message, multiple Task tool uses
Task({ subagent_type: "whetstone:ia-security-sentinel", ... })
Task({ subagent_type: "whetstone:ia-performance-oracle", ... })
Task({ subagent_type: "whetstone:ia-architecture-strategist", ... })

Sequential dispatch (each Task in its own message, waiting on the previous to return) is a serialization bug, not a coordination pattern. If agents truly depend on each other's output, that is a pipeline -- see Coordination Models below.

Bounded parallelism when the harness caps active subagents. Single-message fan-out (above) tells Opus to dispatch in parallel; the harness then decides how many to run concurrently. When the harness accepts the dispatch but caps active execution, queue the overflow rather than failing. Dispatch as many as the harness accepts in the first batch, treat transient capacity-related spawn errors as backpressure (any retryable error indicating the limiter rejected the dispatch — exact wording varies across harness versions and platforms; do not pattern-match on a fixed string list), and re-dispatch queued agents as active ones complete. Record an agent as failed only after a successful dispatch times out or returns an error, or when dispatch fails for a non-capacity reason (bad tool name, malformed prompt, missing permission). The fan-out is still parallel — it is just rate-capped to whatever the harness can run concurrently.

Load the reference for the active harness: quick-reference.md for Claude Code or codex-quick-reference.md for Codex.

Rules for when and how to dispatch agents. Getting these wrong wastes tokens and creates hard-to-debug failures.

Assess 5 signals: file count, module span, dependency chain, risk surface, parallelism potential. If 3+ fall in the "complex" column, dispatch a team. Below 3, do it yourself. When in doubt, prefer the simple path -- team overhead is only justified when parallelism provides a real speedup.

Every task prompt must include these fields to prevent integration failures:

Bound acceptance criteria over a named set, not a deliverable. "Produce a change list" is measurable and still satisfied by a partial answer; "every call site of updated" or "every migration under accounted for" is satisfied only by exhausting the set. Phrase the criterion as the bound wherever the task has a nameable set. Skip this on tasks small enough that the agent sees the whole set at once -- an exhaustiveness bound on a three-file change buys nothing and invites a sweep the task never needed.

One owner per aggregate check. Exclusive file ownership has a verification counterpart: assign the aggregate checks -- full test suite, whole-package typecheck, repo-wide lint -- to exactly one owner per dispatch. That is the integration agent where one exists, otherwise the orchestrator at post-wave reconciliation. Every other agent's Acceptance Criteria names the narrowest checks that prove its own edits (lint/format/typecheck scoped to its owned files, tests covering those files), and its prompt names the aggregate checks it must not run. Duplicate suite runs across a wave are wasted wall-clock, not extra assurance. This is what the parallel-dispatch constraint below leaves unsaid: it tells agents not to run the suite, and this tells them what to run instead.

Cardinal rule: one owner per file. When files must be shared, designate a single owner; other agents send change requests, owner applies sequentially. If an upstream dependency isn't ready yet, write a stub/mock so downstream work can continue unblocked.

Implementation agents share state via git by default, so parallel dispatch causes overwrites. In Claude Code, use . In Codex, create worktrees explicitly with the skill, then give each agent its assigned absolute worktree path; has no argument. Without worktrees, dispatch implementation agents sequentially. Review, research, and analysis agents are safe to parallelize when they remain read-only.

Pre-dispatch file-intersection check -- operationalize the one-owner-per-file rule with a runnable safety gate before every parallel dispatch:

The intersection check catches silent conflicts the controller misses at plan time; the dispatch-prompt constraint catches them when a unit's file list was incomplete.

One implementation unit per worker. A worker dispatched to implement a unit gets a context carrying no prior implementation unit, and it is retired once that unit is integrated -- never retasked onto a second unit, never held as an idle pool. The same handle may continue or recover its own unit (that is the crash-relaunch path below), but a worker that has already reasoned about one unit's constraints carries them into the next as unstated assumptions. This binds implementation dispatch on the subagent surface; the persistent Teammate model above is deliberately long-lived and unaffected, as is the mode-to-mode carry-forward in Context Carry-Forward. Invoke an explicit close or release only where the harness exposes one and assigns that action to the caller, and clean up an isolated workspace only after confirming the unit's work was integrated -- never infer a cleanup command from the provider name.

Preset team compositions: Start from a named preset before designing a custom team. See team-compositions.md for the conceptual Review / Debug / Feature / Fullstack / Migration / Security / Research compositions. Its fields are Claude-specific; in Codex, express the same read-only or implementation boundary in the task prompt and available permissions. Use the smallest preset that covers all required dimensions — overlap between reviewers is a sizing signal to redefine focus areas, not add more agents.

Model selection by task complexity: Apply explicit model arguments only when the active harness exposes them. Claude Code supports the examples below; Codex's collaboration tools currently do not accept a per-agent model argument.

model: "haiku"

model: "opus"

Key the choice on reasoning difficulty, not size: file count, agent count, and wave width are not model triggers. A large mechanical rename stays cheap; a single-file change to a concurrency invariant does not. Escalate for nonlocal invariants, concurrency or state machines, migrations, parsing, auth and security, retry/error semantics, or public API and data-contract changes -- the asymmetry is that over-escalating a mechanical edit costs money while under-escalating a one-file concurrency fix costs a production defect.

When passing work between agents (leader→implementer, implementer→reviewer, reviewer→leader), include:

The controller reads all tasks from the plan upfront and provides full task text directly to subagents. Never make subagents read plan files themselves -- they waste tokens navigating, may read different versions, and inherit unclear context. Paste the task content into the prompt. See handoff-templates.md for QA FAIL and Escalation Report formats.

Include the four statuses defined in (DONE, DONE_WITH_CONCERNS, BLOCKED, NEEDS_CONTEXT) in every teammate prompt so they know the reporting format. Expect teammates to report one. BLOCKED responses get further triage via the decision tree below.

BLOCKED triage decision tree -- when a teammate reports BLOCKED, classify the root cause before acting. Never retry the same prompt on the same model without changing a variable.

Never ignore an escalation. Never force the same agent to retry without changing at least one variable (context, model, or task scope).

An agent that crashed or timed out without returning a usable result is a different case, and the working tree decides the response. Before relaunching, inspect that agent's owned files for partial edits (, ); a clean tree means it never got that far, so treat it as an ordinary retry. Otherwise relaunch once with a prompt that names the files it already touched and instructs verify-and-continue, not redo -- re-dispatching "the same task" to an agent that stopped mid-write produces double-applied edits, duplicated blocks, or a second migration. That relaunch is a retry of the same agent, not a new agent against the dispatch budget, and a second crash for the same agent is a hard stop: report it. Neither a crash nor a timeout licenses calling the run an infrastructure failure to justify a free retry. This path covers in-place edits to owned source files; when the lost output was a declared handoff artifact, the artifact rule in resilience-patterns.md governs instead. An agent-reported BLOCKED is the other case -- it answered, so it routes to the table above.

Verify spec compliance first: does the output match what was requested? Only then evaluate quality. A beautifully written solution to the wrong problem is still wrong. Structure review as two explicit passes -- pass 1 rejects on spec mismatch without reading further, pass 2 assesses correctness and quality on spec-compliant outputs.

Max 3 attempts per task. After each QA failure, pass structured feedback to the implementer using the QA FAIL template. After 3 failures, mark the task as blocked, continue the pipeline (don't halt everything), and let final integration catch remaining issues. Counter resets when advancing to the next task.

Post-integration verification -- after all agents return: check overlapping file edits, review for conflicting approaches, run full test suite.

Spawned-session behavior -- when a skill runs inside an orchestrated pipeline (as a subagent, not user-invoked), suppress interactive prompts, auto-choose the conservative/safe default, and skip upgrade checks and telemetry. (Umbrella term: non-interactive context. Also called "Headless mode" in ia-brainstorming and ia-receiving-code-review.) Focus on completing the task and reporting results via prose output. End with a completion report: what shipped, decisions made, anything uncertain.

Decision presentation -- never silently drop options. Use the active harness's structured question tool when available, otherwise ask in chat. If its option cap cannot represent every viable choice, split the choice into sequential rounds (, , ...) instead of truncating it. Surface cross-option dependencies in the round that introduces them. In spawned sessions, the rule above takes precedence: do not ask; choose the safe default and report it.

Choose context carry-forward through capabilities the active harness exposes. Claude Code can use Continue, Rewind, , Subagent, or +brief; see context-carry-forward.md. In Codex, use a follow-up task for the same agent, a fresh agent with a focused handoff, automatic compaction, or a new thread with a brief. Do not emit Claude slash commands in Codex.

Two approaches to multi-agent coordination exist. Choose based on the work pattern:

For most work, start with stateless handoffs. Graduate to stateful coordination only when parallelism provides a real speedup and you have worktree isolation to prevent file conflicts.

Before designing any multi-agent workflow, check it against the four named failure modes in dispatch-anti-patterns.md: router persona, persona calls persona, sequential paraphraser, deep persona trees. Rule of thumb: if the proposed swarm has more coordinator roles than worker roles, collapse it.

When dispatching judge panels, running parallel reviewers, or iterating on subjective evaluations, load anti-sycophancy.md — cold-start isolation, fresh instances per round, label randomization, convergence detection.

When designing multi-agent workflows that must survive partial failure, load resilience-patterns.md — cascade prevention (timeouts, circuit breakers, bulkheads), failure classification (retry vs reassign vs escalate), mid-pipeline compensation for irreversible side effects, post-failure synthesis of partial results.