Writing Agent Relay Workflows

Overview

The relay broker-sdk workflow system orchestrates multiple AI agents (Claude, Codex, Gemini, Aider, Goose) through typed DAG-based workflows. Workflows can be written in TypeScript (preferred), Python, or YAML.

Language preference: TypeScript > Python > YAML. Use TypeScript unless the project is Python-only or a simple config-driven workflow suits YAML.

Pattern selection: Do not default to dag blindly. If the job needs a different swarm/workflow type, consult the choosing-swarm-patterns skill when available and select the pattern that best matches the coordination problem.

When to Use

• Building multi-agent workflows with step dependencies
• Orchestrating different AI CLIs (claude, codex, gemini, aider, goose)
• Creating DAG, pipeline, fan-out, or other swarm patterns
• Needing verification gates, retries, or step output chaining
• Dynamic channel management: agents joining/leaving/muting channels mid-workflow

Quick Reference

import { workflow } from '@agent-relay/sdk/workflows';

const result = await workflow('my-workflow')
  .description('What this workflow does')
  .pattern('dag') // or 'pipeline', 'fan-out', etc.
  .channel('wf-my-workflow') // dedicated channel (auto-generated if omitted)
  .maxConcurrency(3)
  .timeout(3_600_000) // global timeout (ms)

  .agent('lead', { cli: 'claude', role: 'Architect', retries: 2 })
  .agent('worker', { cli: 'codex', role: 'Implementer', retries: 2 })

  .step('plan', {
    agent: 'lead',
    task: `Analyze the codebase and produce a plan.`,
    retries: 2,
    verification: { type: 'output_contains', value: 'PLAN_COMPLETE' },
  })
  .step('implement', {
    agent: 'worker',
    task: `Implement based on this plan:\n{{steps.plan.output}}`,
    dependsOn: ['plan'],
    verification: { type: 'exit_code' },
  })

  .onError('retry', { maxRetries: 2, retryDelayMs: 10_000 })
  .run({ cwd: process.cwd() });

console.log('Result:', result.status);

Critical TypeScript rules:

1. Check the project's package.json for "type": "module" — if ESM, use import and top-level await. If CJS, use require() and wrap in async function main().
2. agent-relay run <file.ts> executes the file as a standalone subprocess — it does NOT inspect exports. The file MUST call .run().
3. Use .run({ cwd: process.cwd() }) — createWorkflowRenderer does not exist
4. Validate with --dry-run before running: agent-relay run --dry-run workflow.ts

⚡ Parallelism — Design for Speed

This is the most important design consideration. Sequential workflows waste hours. Always design for maximum parallelism.

Cross-Workflow Parallelism: Wave Planning

When a project has multiple workflows, group independent ones into parallel waves:

# BAD — sequential (14 hours for 27 workflows at ~30 min each)
agent-relay run workflows/34-sst-wiring.ts
agent-relay run workflows/35-env-config.ts
agent-relay run workflows/36-loading-states.ts
# ... one at a time

# GOOD — parallel waves (3-4 hours for 27 workflows)
# Wave 1: independent infra (parallel)
agent-relay run workflows/34-sst-wiring.ts &
agent-relay run workflows/35-env-config.ts &
agent-relay run workflows/36-loading-states.ts &
agent-relay run workflows/37-responsive.ts &
wait
git add -A && git commit -m "Wave 1"

# Wave 2: testing (parallel — independent test suites)
agent-relay run workflows/40-unit-tests.ts &
agent-relay run workflows/41-integration-tests.ts &
agent-relay run workflows/42-e2e-tests.ts &
wait
git add -A && git commit -m "Wave 2"

Wave Planning Heuristics

Two workflows can run in parallel if they don't have write-write or write-read file conflicts:

Touch Zone	Can Parallelize?
Different packages/*/src/ dirs	✅ Yes
Different app/ routes	✅ Yes
Same package, different subdirs	⚠️ Usually yes
Same files (shared config, root package.json)	❌ No — sequential or same wave with merge
Explicit dependency	❌ No — ordered waves

Declare File Scope for Planning

Help wave planners (human or automated) understand what each workflow touches:

workflow('48-comparison-mode')
  .packages(['web', 'core']) // monorepo packages touched
  .isolatedFrom(['49-feedback-system']) // explicitly safe to parallelize
  .requiresBefore(['46-admin-dashboard']); // explicit ordering constraint

Within-Workflow Parallelism

Use shared dependsOn to fan out independent sub-tasks:

// BAD — unnecessary sequential chain
.step('fix-component-a', { agent: 'worker', dependsOn: ['review'] })
.step('fix-component-b', { agent: 'worker', dependsOn: ['fix-component-a'] })  // why wait?

// GOOD — parallel fan-out, merge at the end
.step('fix-component-a', { agent: 'impl-1', dependsOn: ['review'] })
.step('fix-component-b', { agent: 'impl-2', dependsOn: ['review'] })  // same dep = parallel
.step('verify-all', { agent: 'reviewer', dependsOn: ['fix-component-a', 'fix-component-b'] })

Impact

Real-world example (Relayed — 60 workflows):

• Sequential: ~30 min × 60 = 30 hours
• Parallel waves (4-6 per wave): ~12 waves × 35 min = ~7 hours (4x faster)
• Aggressive parallelism (8-way): ~4 hours (7.5x faster)

---

Failure Prevention

These workflow files are easy to break in ways that only appear mid-run. Follow these rules when authoring or editing workflow .ts files.

1. Do not use raw top-level await

Executor-driven workflow files may be run through a tsx/esbuild path that behaves like CJS. Raw top-level await can fail with:

• Top-level await is currently not supported with the "cjs" output format

Always wrap execution like this:

async function runWorkflow() {
  const result = await workflow('my-workflow')
    // ...
    .run({ cwd: process.cwd() });

  console.log('Workflow status:', result.status);
}

runWorkflow().catch((error) => {
  console.error(error);
  process.exit(1);
});

Do not end workflow files with bare top-level await workflow(...).run(...).

2. Avoid raw fenced code blocks inside workflow task template literals

Raw triple-backtick code fences inside large inline task: \...`template strings are fragile and can break outer TypeScript parsing, especially when they contain language tags likeswiftordiff`.

Preferred options, in order:

1. Avoid inline fenced examples entirely
2. Move larger examples to referenced files
3. Use plain indented examples instead of fenced blocks
4. If fenced blocks must exist inside generated inner code, escape them consistently and syntax-check the outer workflow file afterward

3. Keep final verification boring and deterministic

Final verification should validate real outputs with simple, portable shell commands. If checking for multiple symbols, use extended regex explicitly:

grep -Eq "foo|bar|baz" file.ts

Do not rely on basic grep alternation like:

grep -c "foo\|bar\|baz" file.ts

That can silently misbehave and create fake failures even when the generated code is correct.

4. Separate durable outputs from execution exhaust

Commit:

• generated product code
• migrations
• tests
• docs
• workflow-definition fixes

Do not commit by default:

• .logs/
• transient executor output
• retry artifacts
• temporary step-output files

5. Prefer Codex for implementation-heavy roles and Claude for review

Default team split for workflow-authored agent roles:

• lead / implementer / writer / fixer → codex
• reviewer → claude

Use Claude as the primary implementer only when there is a specific reason.

6. Be explicit about shell requirements

If executor scripts use Bash-only features such as associative arrays, require modern Bash explicitly. On macOS, prefer a known-good Bash path when needed, for example:

/opt/homebrew/bin/bash workflows/your-workflow/execute.sh --wave 2

7. Make resume semantics explicit

Document clearly whether the executor supports:

• full-run continuation
• --wave
• --workflow
• --resume

Do not assume users will infer the behavior. In particular, --wave N should be understood as "run only this wave" unless the executor explicitly chains onward.

8. Syntax-check workflow files after editing

After editing workflow .ts files, run a lightweight syntax check before launching a large batch run. This is especially important if the workflow contains:

• large inline task template literals
• embedded code examples
• escaped backticks
• wrapper changes around workflow execution

---

End-to-End Bug Fix Workflows

For bug-fix or reliability workflows, do not stop at unit or integration tests. The workflow should explicitly prove that the original user-visible problem is fixed.

Required phases for fix workflows

1. Capture the original failure
   • Reproduce the bug first in a deterministic or evidence-capturing step
   • Save exact commands, logs, status codes, or screenshots/artifacts
2. State the acceptance contract
   • Define the exact end-to-end success criteria before implementation
   • Include the real entrypoint a user would run
3. Implement the fix
4. Rebuild / reinstall from scratch
   • Do not trust dirty local state
   • Prefer a clean environment when install/bootstrap behavior is involved
5. Run targeted regression checks
   • Unit/integration tests are helpful but not sufficient by themselves
6. Run a full end-to-end validation
   • Use the real CLI / API / install path
   • Prefer a clean environment (Docker, sandbox, cloud workspace, Daytona, etc.) for install/runtime issues
7. Compare before vs after evidence
   • Show that the original failure no longer occurs
8. Record residual risks
   • Call out what was not covered

Clean-environment validation guidance

When the bug involves install, bootstrap, PATH/shims, auth, brokers, background services, OS-specific packaging, or first-run UX, add a second workflow (or second phase) that validates the fix in a fresh environment.

Preferred order of proving environments:

1. disposable sandbox / cloud workspace
2. Docker / containerized environment
3. fresh local shell with isolated paths

Meta-workflow guidance

If the right proving environment is unclear, first write a meta-workflow that:

• compares candidate validation environments
• defines the acceptance contract
• chooses the best swarm pattern
• then authors the final fix/validation workflow

This is often better than jumping straight to implementation.

Key Concepts

Step Output Chaining

Use {{steps.STEP_NAME.output}} in a downstream step's task to inject the prior step's terminal output.

Only chain output from clean sources:

• Deterministic steps (shell commands — always clean)
• Non-interactive agents (preset: 'worker' — clean stdout)

Never chain from interactive agents (cli: 'claude' without preset) — PTY output includes spinners, ANSI codes, and TUI chrome. Instead, have the agent write to a file, then read it in a deterministic step.

Verification Gates

verification: { type: 'exit_code' }                        // preferred for code-editing steps
verification: { type: 'output_contains', value: 'DONE' }   // optional accelerator
verification: { type: 'file_exists', value: 'src/out.ts' } // deterministic file check

Only these four types are valid: exit_code, output_contains, file_exists, custom. Invalid types are silently ignored and fall through to process-exit auto-pass.

Verification token gotcha: If the token appears in the task text, the runner requires it twice in output (once from task echo, once from agent). Prefer exit_code for code-editing steps to avoid this.

DAG Dependencies

Steps with dependsOn wait for all listed steps. Steps with no dependencies start immediately. Steps sharing the same dependsOn run in parallel:

.step('fix-types',  { agent: 'worker', dependsOn: ['review'], ... })
.step('fix-tests',  { agent: 'worker', dependsOn: ['review'], ... })
.step('final',      { agent: 'lead',   dependsOn: ['fix-types', 'fix-tests'], ... })

Self-Termination

Do NOT add exit instructions to task strings. The runner handles this automatically.

Step Completion Model

Steps complete through a multi-signal pipeline (highest priority first):

1. Deterministic verification — exit_code, file_exists, output_contains pass → immediate completion
2. Owner decision — OWNER_DECISION: COMPLETE|INCOMPLETE_RETRY|INCOMPLETE_FAIL
3. Evidence-based — channel signals, file artifacts, clean exit code
4. Marker fast-path — STEP_COMPLETE:<step-name> (optional accelerator)
5. Process-exit fallback — agent exits 0 with no signals → completes after grace period

Key principle: No single signal is mandatory. Describe the deliverable, not what to print.

Dynamic Channel Management

Agents can dynamically subscribe, unsubscribe, mute, and unmute channels after spawn. This eliminates the need for client-side channel filtering and manual peer fanout.

SDK API

// Subscribe an agent to additional channels post-spawn
relay.subscribe({ agent: 'security-auditor', channels: ['review-pr-456'] });

// Unsubscribe — agent leaves the channel entirely
relay.unsubscribe({ agent: 'security-auditor', channels: ['general'] });

// Mute — agent stays subscribed (history access) but messages are NOT injected into PTY
relay.mute({ agent: 'security-auditor', channel: 'review-pr-123' });

// Unmute — resume PTY injection
relay.unmute({ agent: 'security-auditor', channel: 'review-pr-123' });

Agent-level methods are also available:

const agent = await relay.claude.spawn({ name: 'auditor', channels: ['ch-a'] });
await agent.subscribe(['ch-b']); // now subscribed to ch-a and ch-b
await agent.mute('ch-a'); // ch-a messages silenced (still in history)
await agent.unmute('ch-a'); // ch-a messages resume
await agent.unsubscribe(['ch-b']); // leaves ch-b
console.log(agent.channels); // ['ch-a']
console.log(agent.mutedChannels); // []

Semantics

Operation	Channel membership	PTY injection	History access
subscribe	Yes	Yes	Yes
unsubscribe	No	No	No (leaves)
mute	Yes (stays)	No (silenced)	Yes (can query)
unmute	Yes	Yes (resumes)	Yes

Events

relay.onChannelSubscribed = (agent, channels) => {
  /* ... */
};
relay.onChannelUnsubscribed = (agent, channels) => {
  /* ... */
};
relay.onChannelMuted = (agent, channel) => {
  /* ... */
};
relay.onChannelUnmuted = (agent, channel) => {
  /* ... */
};

When to Use in Workflows

• Multi-PR chat sessions: Agents focused on one PR can mute other PR channels to reduce noise
• Phase transitions: Subscribe agents to new channels as work progresses between phases
• Team isolation: Workers mute the main coordination channel during focused work, unmute for review
• Dynamic fanout: A lead subscribes workers to sub-channels at runtime based on task decomposition

What This Eliminates

With broker-managed subscriptions, you no longer need:

1. Client-side persona filtering (personaNames.has(from) checks)
2. Channel prefix regex for message routing
3. Manual peer fanout (iterating agents to forward messages)
4. Dedup caches for dual-path delivery

Agent Definition

.agent('name', {
  cli: 'claude' | 'codex' | 'gemini' | 'aider' | 'goose' | 'opencode' | 'droid',
  role?: string,
  preset?: 'lead' | 'worker' | 'reviewer' | 'analyst',
  retries?: number,
  model?: string,
  interactive?: boolean, // default: true
})

Model Constants

Always use model constants from @agent-relay/config instead of string literals. Each CLI has a typed constants object with its available models:

import { ClaudeModels, CodexModels, GeminiModels } from '@agent-relay/config';

.agent('planner', { cli: 'claude', model: ClaudeModels.OPUS })    // not 'opus'
.agent('worker',  { cli: 'claude', model: ClaudeModels.SONNET })  // not 'sonnet'
.agent('coder',   { cli: 'codex',  model: CodexModels.GPT_5_4 })  // not 'gpt-5.4'

Post-spawn channel operations (available on Agent instances and AgentRelay facade):

// Agent instance methods
agent.subscribe(channels: string[]): Promise<void>
agent.unsubscribe(channels: string[]): Promise<void>
agent.mute(channel: string): Promise<void>
agent.unmute(channel: string): Promise<void>
agent.channels: string[]          // current subscribed channels
agent.mutedChannels: string[]     // currently muted channels

// AgentRelay facade methods (by agent name)
relay.subscribe({ agent: string, channels: string[] }): Promise<void>
relay.unsubscribe({ agent: string, channels: string[] }): Promise<void>
relay.mute({ agent: string, channel: string }): Promise<void>
relay.unmute({ agent: string, channel: string }): Promise<void>

Preset	Interactive	Relay access	Use for
lead	yes (PTY)	yes	Coordination, monitoring channels
worker	no (subprocess)	no	Bounded tasks, structured stdout
reviewer	no (subprocess)	no	Reading artifacts, producing verdicts
analyst	no (subprocess)	no	Reading code/files, writing findings

Non-interactive presets run via one-shot mode (claude -p, codex exec). Output is clean and available via {{steps.X.output}}.

Critical rule: Pre-inject content into non-interactive agents. Don't ask them to read large files — pre-read in a deterministic step and inject via {{steps.X.output}}.

Step Definition

Agent Steps

.step('name', {
  agent: string,
  task: string,                   // supports {{var}} and {{steps.NAME.output}}
  dependsOn?: string[],
  verification?: VerificationCheck,
  retries?: number,
})

Deterministic Steps (Shell Commands)

.step('verify-files', {
  type: 'deterministic',
  command: 'test -f src/auth.ts && echo "FILE_EXISTS"',
  dependsOn: ['implement'],
  captureOutput: true,
  failOnError: true,
})

Use for: file checks, reading files for injection, build/test gates, git operations.

Common Patterns

Interactive Team (lead + workers on shared channel)

When a task involves creating/modifying multiple files with review feedback, use interactive agents on a shared channel instead of non-interactive one-shot workers. The lead coordinates, reviews, and posts feedback; workers implement and iterate.

.agent('lead', {
  cli: 'claude',
  model: ClaudeModels.OPUS,
  role: 'Architect and reviewer — assigns work, reviews, posts feedback',
  retries: 1,
  // No preset — interactive by default
})

.agent('impl-new', {
  cli: 'codex',
  model: CodexModels.O3,
  role: 'Creates new files. Listens on channel for assignments and feedback.',
  retries: 2,
  // No preset — interactive, receives channel messages
})

.agent('impl-modify', {
  cli: 'codex',
  model: CodexModels.O3,
  role: 'Edits existing files. Listens on channel for assignments and feedback.',
  retries: 2,
})

// All three share the same dependsOn — they start concurrently (no deadlock)
.step('lead-coordinate', {
  agent: 'lead',
  dependsOn: ['context'],
  task: `You are the lead on #channel. Workers: impl-new, impl-modify.
Post the plan. Assign files. Review their work. Post feedback if needed.
Workers iterate based on your feedback. Exit when all files are correct.`,
})
.step('impl-new-work', {
  agent: 'impl-new',
  dependsOn: ['context'],   // same dep as lead = parallel start
  task: `You are impl-new on #channel. Wait for the lead's plan.
Create files as assigned. Report completion. Fix issues from feedback.`,
})
.step('impl-modify-work', {
  agent: 'impl-modify',
  dependsOn: ['context'],   // same dep as lead = parallel start
  task: `You are impl-modify on #channel. Wait for the lead's plan.
Edit files as assigned. Report completion. Fix issues from feedback.`,
})
// Downstream gates on lead (lead exits when satisfied)
.step('verify', { type: 'deterministic', dependsOn: ['lead-coordinate'], ... })

Key behaviors observed in production:

• Workers self-organize from channel context. Workers read each other's completion messages and start dependent work without waiting for the lead to relay. The shared channel gives them ambient awareness.
• Lead-as-reviewer is more efficient than a separate reviewer agent. The lead reads actual files and runs typecheck between rounds — one agent doing coordination + review eliminates a step.
• Codex interactive mode works well with PTY channel injection. Don't default to preset: 'worker' — interactive Codex agents receive and act on channel messages reliably.
• Workers may outpace the lead. If the lead is reviewing while workers are fast, the lead's "proceed" message may arrive after the worker already started from channel context. This is harmless but worth knowing.
• No feedback loop needed = fast path. If workers get it right first try, the interactive pattern completes just as fast as one-shot. The feedback loop is insurance, not overhead.

When to use interactive team vs one-shot DAG:

Scenario	Pattern
4+ files, likely needs iteration	Interactive team
Simple edits, well-specified	One-shot DAG with preset: 'worker'
Cross-agent review feedback loop	Interactive team
Independent tasks, no coordination	Fan-out with non-interactive workers

Pipeline (sequential handoff)

.pattern('pipeline')
.step('analyze', { agent: 'analyst', task: '...' })
.step('implement', { agent: 'dev', task: '{{steps.analyze.output}}', dependsOn: ['analyze'] })
.step('test', { agent: 'tester', task: '{{steps.implement.output}}', dependsOn: ['implement'] })

Error Handling

.onError('fail-fast')   // stop on first failure (default)
.onError('continue')    // skip failed branches, continue others
.onError('retry', { maxRetries: 3, retryDelayMs: 5000 })

Multi-File Edit Pattern

When a workflow needs to modify multiple existing files, use one agent step per file with a deterministic verify gate after each. Agents reliably edit 1-2 files per step but fail on 4+.

steps:
  - name: read-types
    type: deterministic
    command: cat src/types.ts
    captureOutput: true

  - name: edit-types
    agent: dev
    dependsOn: [read-types]
    task: |
      Edit src/types.ts. Current contents:
      {{steps.read-types.output}}
      Add 'pending' to the Status union type.
      Only edit this one file.
    verification:
      type: exit_code

  - name: verify-types
    type: deterministic
    dependsOn: [edit-types]
    command: 'if git diff --quiet src/types.ts; then echo "NOT MODIFIED"; exit 1; fi; echo "OK"'
    failOnError: true

  - name: read-service
    type: deterministic
    dependsOn: [verify-types]
    command: cat src/service.ts
    captureOutput: true

  - name: edit-service
    agent: dev
    dependsOn: [read-service]
    task: |
      Edit src/service.ts. Current contents:
      {{steps.read-service.output}}
      Add a handlePending() method.
      Only edit this one file.
    verification:
      type: exit_code

  - name: verify-service
    type: deterministic
    dependsOn: [edit-service]
    command: 'if git diff --quiet src/service.ts; then echo "NOT MODIFIED"; exit 1; fi; echo "OK"'
    failOnError: true

  # Deterministic commit — never rely on agents to commit
  - name: commit
    type: deterministic
    dependsOn: [verify-service]
    command: git add src/types.ts src/service.ts && git commit -m "feat: add pending status"
    failOnError: true

Key rules:

• Read the file in a deterministic step right before the edit (not all files upfront)
• Tell the agent "Only edit this one file" to prevent it touching other files
• Verify with git diff --quiet after each edit — fail fast if the agent didn't write
• Always commit with a deterministic step, never an agent step

File Materialization: Verify Before Proceeding

After any step that creates files, add a deterministic file_exists check before proceeding. Non-interactive agents may exit 0 without writing anything (wrong cwd, stdout instead of disk).

- name: verify-files
  type: deterministic
  dependsOn: [impl-auth, impl-storage]
  command: |
    missing=0
    for f in src/auth/credentials.ts src/storage/client.ts; do
      if [ ! -f "$f" ]; then echo "MISSING: $f"; missing=$((missing+1)); fi
    done
    if [ $missing -gt 0 ]; then echo "$missing files missing"; exit 1; fi
    echo "All files present"
  failOnError: true

Rules for file-writing tasks:

1. Use full paths from project root — say src/auth/credentials.ts, not credentials.ts
2. Add IMPORTANT: Write the file to disk. Do NOT output to stdout.
3. Use file_exists verification for creation steps (not just exit_code)
4. Gate all downstream steps on the verify step

DAG Deadlock Anti-Pattern

# WRONG — deadlock: coordinate depends on context, work-a depends on coordinate
steps:
  - name: coordinate
    dependsOn: [context]    # lead waits for WORKER_DONE...
  - name: work-a
    dependsOn: [coordinate] # ...but work-a can't start until coordinate finishes

# RIGHT — workers and lead start in parallel
steps:
  - name: context
    type: deterministic
  - name: work-a
    dependsOn: [context]    # starts with lead
  - name: coordinate
    dependsOn: [context]    # starts with workers
  - name: merge
    dependsOn: [work-a, coordinate]

Rule: if a lead step's task mentions downstream step names alongside waiting keywords, that's a deadlock.

Step Sizing

One agent, one deliverable. A step's task prompt should be 10-20 lines max.

Split into a lead + workers team when:

• The task requires a 50+ line prompt
• The deliverable is multiple files that must be consistent
• You need one agent to verify another's output

# Team pattern: lead + workers on a shared channel
steps:
  - name: track-lead-coord
    agent: track-lead
    dependsOn: [prior-step]
    task: |
      Lead the track on #my-track. Workers: track-worker-1, track-worker-2.
      Post assignments to the channel. Review worker output.

  - name: track-worker-1-impl
    agent: track-worker-1
    dependsOn: [prior-step] # same dep as lead — starts concurrently
    task: |
      Join #my-track. track-lead will post your assignment.
      Implement the file as directed.
    verification:
      type: exit_code

  - name: next-step
    dependsOn: [track-lead-coord] # downstream depends on lead, not workers

Supervisor Pattern

When you set .pattern('supervisor') (or hub-spoke, fan-out), the runner auto-assigns a supervisor agent as owner for worker steps. The supervisor monitors progress, nudges idle workers, and issues OWNER_DECISION.

Auto-hardening only activates for hub patterns — not pipeline or dag.

Use case	Pattern	Why
Sequential, no monitoring	pipeline	Simple, no overhead
Workers need oversight	supervisor	Auto-owner monitors
Local/small models	supervisor	Supervisor catches stuck workers
All non-interactive	pipeline or dag	No PTY = no supervision needed

Concurrency

Cap maxConcurrency at 4-6. Spawning 10+ agents simultaneously causes broker timeouts.

Parallel agents	maxConcurrency
2-4	4 (default safe)
5-10	5
10+	6-8 max

Common Mistakes

Mistake	Fix
All workflows run sequentially	Group independent workflows into parallel waves (4-7x speedup)
Every step depends on the previous one	Only add dependsOn when there's a real data dependency
Self-review step with no timeout	Set timeout: 300_000 (5 min) — Codex hangs in non-interactive review
One giant workflow per feature	Split into smaller workflows that can run in parallel waves
Adding exit instructions to tasks	Runner handles self-termination automatically
Setting timeoutMs on agents/steps	Use global .timeout() only
Using general channel	Set .channel('wf-name') for isolation
{{steps.X.output}} without dependsOn: ['X']	Output won't be available yet
Requiring exact sentinel as only completion gate	Use exit_code or file_exists verification
Writing 100-line task prompts	Split into lead + workers on a channel
maxConcurrency: 16 with many parallel steps	Cap at 5-6
Non-interactive agent reading large files via tools	Pre-read in deterministic step, inject via {{steps.X.output}}
Workers depending on lead step (deadlock)	Both depend on shared context step
fan-out/hub-spoke for simple parallel workers	Use dag instead
pipeline but expecting auto-supervisor	Only hub patterns auto-harden. Use .pattern('supervisor')
Workers without preset: 'worker' in one-shot DAG lead+worker flows	Add preset for clean stdout when chaining {{steps.X.output}} (not needed for interactive team patterns)
Using _ in YAML numbers (timeoutMs: 1_200_000)	YAML doesn't support _ separators
Workflow timeout under 30 min for complex workflows	Use 3600000 (1 hour) as default
Using require() in ESM projects	Check package.json for "type": "module" — use import if ESM
Wrapping in async function main() in ESM	ESM supports top-level await — no wrapper needed
Using createWorkflowRenderer	Does not exist. Use .run({ cwd: process.cwd() })
export default workflow(...)...build()	No .build(). Chain ends with .run() — the file must call .run(), not just export config
Relative import '../workflows/builder.js'	Use import { workflow } from '@agent-relay/sdk/workflows'
Hardcoded model strings (model: 'opus')	Use constants: import { ClaudeModels } from '@agent-relay/config' → model: ClaudeModels.OPUS
Thinking agent-relay run inspects exports	It executes the file as a subprocess. Only .run() invocations trigger steps
pattern('single') on cloud runner	Not supported — use dag
pattern('supervisor') with one agent	Same agent is owner + specialist. Use dag
Invalid verification type (type: 'deterministic')	Only exit_code, output_contains, file_exists, custom are valid
Chaining {{steps.X.output}} from interactive agents	PTY output is garbled. Use deterministic steps or preset: 'worker'
Single step editing 4+ files	Agents modify 1-2 then exit. Split to one file per step with verify gates
Relying on agents to git commit	Agents emit markers without running git. Use deterministic commit step
File-writing steps without file_exists verification	exit_code auto-passes even if no file written
Manual peer fanout in handleChannelMessage()	Use broker-managed channel subscriptions — broker fans out to all subscribers automatically
Client-side personaNames.has(from) filtering	Use relay.subscribe()/relay.unsubscribe() — only subscribed agents receive messages
Agents receiving noisy cross-channel messages during focused work	Use relay.mute({ agent, channel }) to silence non-primary channels without leaving them
Hardcoding all channels at spawn time	Use agent.subscribe() / agent.unsubscribe() for dynamic channel membership post-spawn
Using preset: 'worker' for Codex in interactive team patterns when coordination is needed	Codex interactive mode works fine with PTY channel injection. Drop the preset for interactive team patterns (keep it for one-shot DAG workers where clean stdout matters)
Separate reviewer agent from lead in interactive team	Merge lead + reviewer into one interactive Claude agent — reviews between rounds, fewer agents
Not printing PR URL after gh pr create	Add a final deterministic step: echo "PR: $(cat pr-url.txt)" or capture in the gh pr create command
Workflow ending without worktree + PR for cross-repo changes	Add setup-worktree at start and push-and-pr + cleanup-worktree at end

YAML Alternative

version: '1.0'
name: my-workflow
swarm:
  pattern: dag
  channel: wf-my-workflow
agents:
  - name: lead
    cli: claude
    role: Architect
  - name: worker
    cli: codex
    role: Implementer
workflows:
  - name: default
    steps:
      - name: plan
        agent: lead
        task: 'Produce a detailed implementation plan.'
      - name: implement
        agent: worker
        task: 'Implement: {{steps.plan.output}}'
        dependsOn: [plan]
        verification:
          type: exit_code

Run with: agent-relay run path/to/workflow.yaml

Available Swarm Patterns

dag (default), fan-out, pipeline, hub-spoke, consensus, mesh, handoff, cascade, debate, hierarchical, map-reduce, scatter-gather, supervisor, reflection, red-team, verifier, auction, escalation, saga, circuit-breaker, blackboard, swarm

See skill choosing-swarm-patterns for pattern selection guidance.