ia-orchestrating-swarms

name	ia-orchestrating-swarms
class	workflow
description	Coordinate multi-agent swarms for parallel and pipeline workflows. Use when coordinating multiple agents, running parallel reviews, building pipeline workflows, or implementing divide-and-conquer patterns with subagents.

Swarm orchestration

Primitives

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.

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Two Ways to Spawn Agents

Resolve the host primitives before dispatching:

• Claude Code: Task(...) for short-lived subagents; Teammate(...) plus named Task(...) for persistent teams.
• Codex: spawn_agent(...) for short-lived subagents; send_message(...), followup_task(...), and wait_agent(...) for coordination. Use persistent teammates only when the active Codex environment exposes that capability.
• Other harnesses: use their native subagent surface. If none exists, execute the units sequentially in the main thread.

The comparison and examples immediately below describe Claude's two dispatch modes. Codex uses spawn_agent 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.

Aspect	Task (subagent)	Task + team_name + name (teammate)
Lifespan	Until task complete	Until shutdown requested
Communication	Return value	Inbox messages
Task access	None	Shared task list
Team membership	No	Yes
Coordination	One-off	Ongoing
Best for	Searches, analysis, focused work	Parallel work, pipelines, collaboration

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.

Parallel Fan-Out (for independent work)

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 Task calls in one assistant message. In Codex, issue the spawn_agent 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: "security-sentinel", ... })
Task({ subagent_type: "performance-oracle", ... })
Task({ subagent_type: "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.

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Quick Reference

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

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Dispatch Discipline

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

When to dispatch a team vs. do it yourself:

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.

Task description template (for every dispatched task):

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

• Objective: what to accomplish (one sentence)
• Owned Files: files this agent creates or modifies (exclusive -- no file assigned to multiple agents)
• Interface Contracts: what to import from other agents' work, what to export for downstream agents
• Acceptance Criteria: how the agent knows the task is correct
• Out of Scope: what NOT to touch, even if it looks related

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.

No parallel implementation agents (without worktrees):

Implementation agents share state via git by default, so parallel dispatch causes overwrites. In Claude Code, use isolation: "worktree". In Codex, create worktrees explicitly with the ia-git-worktree skill, then give each agent its assigned absolute worktree path; spawn_agent has no isolation 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:

1. Collect each unit's declared Owned Files / Test Paths / Modify Paths from its task spec.
2. Build a {file → unit} map. If any file appears under more than one unit, the dispatch is unsafe. Quick check on Markdown task specs:

   grep -h "^Owned Files:" -A 20 tasks/*.md | grep -v "^Owned Files:" | grep -v "^--$" | sort | uniq -d
   

   Any output is an overlapping file path that needs resolution.
3. On overlap: either downgrade to serial, isolate each unit in a harness-supported worktree, or rewrite unit boundaries so files become exclusive.
4. Even with no declared overlap, include this constraint verbatim in every parallel-dispatch prompt: "Do not run git add, git commit, or the project's test suite while other parallel agents are active -- you'd race on the git index or thrash the test cache. Stage changes for the orchestrator to commit after integration."

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.

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 subagent_type 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.

Task shape	Model
1-2 files, clear spec, mechanical	model: "haiku"
Multi-file integration, standard complexity	Default model
Architecture decisions, ambiguous scope, review	model: "opus"

Handoff protocol -- structured agent-to-agent transfers:

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

1. Context: what was done, relevant files, constraints discovered
2. Deliverable: specific output expected from the receiving agent
3. Acceptance criteria: how the receiving agent knows the work is correct

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.

Standardize implementer status signals:

Include the four statuses defined in ia-verification-before-completion (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.

Root cause	Signal	Response
Missing context	Agent asked for a file, spec, or decision it needed	Provide the missing context, re-dispatch same agent
Reasoning ceiling	Agent attempted, got stuck on a subtlety it cannot resolve	If supported, escalate the model; otherwise narrow the task or provide stronger evidence and re-dispatch
Task too large	Agent made partial progress but hit token/complexity limits	Split into smaller tasks with explicit interface contracts
Spec wrong	Agent surfaces a contradiction in the plan or a missing requirement	Escalate to the user -- do not re-dispatch

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

Two-stage review gate on subagent outputs:

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.

QA retry loop:

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.

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Integration Rules

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 (D1.1, D1.2, ...) 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.

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Context Carry-Forward

Choose context carry-forward through capabilities the active harness exposes. Claude Code can use Continue, Rewind, /compact, Subagent, or /clear+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.

Coordination Models

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

Aspect	Stateless (copy-paste outputs)	Stateful (file ownership + dependencies)
How agents share state	Leader copies full outputs between prompts	Agents read/write shared task files, claim ownership
Best for	Short pipelines, 2-3 agents, sequential handoffs	Parallel work, 4+ agents, complex dependency graphs
Failure mode	Context grows linearly with agent count	Concurrent modification conflicts
Mitigation	Summarize before passing (keep essentials, drop navigation)	Use worktrees or exclusive file ownership per agent

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.

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Dispatch Anti-Patterns

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.

Anti-Sycophancy and Resilience

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.

Verify

• All tasks in terminal state (completed or blocked)
• No orphaned teammates (git worktree list shows no stale entries)
• Overlapping file edits reviewed and merged
• Full test suite passes post-integration

References

Document	When to load	What it covers
team-compositions.md	Sizing a team or choosing a preset	7 preset compositions, subagent_type cardinal rule, custom-team guidelines
agent-types.md	Claude Code agent types	Built-in and plugin subagent_type examples
teammate-operations.md	Claude Code persistent teammates	All 13 operations (spawnTeam, write, broadcast, requestShutdown, etc.)
task-system.md	Claude Code work items and dependencies	TaskCreate, TaskList, TaskGet, TaskUpdate, file structure
codex-quick-reference.md	Codex collaboration calls	Spawn, message, follow up, wait, and worktree guidance
message-formats.md	Sending structured messages between agents	All JSON message examples (regular, shutdown, idle, plan approval)
orchestration-patterns.md	Designing a multi-agent workflow	6 patterns + 3 complete workflow examples
spawn-backends.md	Troubleshooting agent spawn issues	Backend comparison, auto-detection, in-process/tmux/iterm2
environment-config.md	Configuring team environment	Environment variables and team config structure
handoff-templates.md	Passing work between agents	QA FAIL and Escalation Report formats
context-carry-forward.md	Claude Code context controls	Continue / Rewind / compact / Subagent / clear+brief decision table
anti-sycophancy.md	Judge panels, parallel reviewers, subjective evals	Cold-start isolation, fresh instances per round, label randomization, convergence detection
resilience-patterns.md	Designing workflows that survive partial failure	Cascade prevention, failure classification, mid-pipeline compensation, post-failure synthesis