Multi-Agent Orchestration Patterns for AI Developers

Multi-agent systems are more powerful than single agents for complex tasks — but only if you orchestrate them correctly. The wrong pattern leads to wasted API calls, contradictory outputs, or incomplete results.

This post covers six battle-tested orchestration patterns, when to use each, and how to implement them with Attune AI.

Pattern 1: Parallel Execution

When to use: Independent tasks that don't depend on each other's output.

Example: Run a security audit and performance audit at the same time.

from attune.orchestration import ParallelWorkflowRunner

runner = ParallelWorkflowRunner([
    "security",
    "perf-audit",
    "dependency-check",
])

# All three run simultaneously
results = await runner.execute({"path": "src/"})

# Results are keyed by workflow name
print(results["security"].findings)
print(results["perf-audit"].bottlenecks)

Pros: Fastest execution — wall-clock time equals the slowest agent.

Cons: No information sharing between agents.

Pattern 2: Sequential Pipeline

When to use: Each agent builds on the previous agent's output.

Example: Security scan → generate fixes → write tests for the fixes.

from attune.orchestration import SequentialPipeline

pipeline = SequentialPipeline([
    "security",       # Step 1: Find vulnerabilities
    "refactor-plan",  # Step 2: Plan fixes based on findings
    "test-gen",       # Step 3: Generate tests for the fixes
])

result = await pipeline.execute({"path": "src/"})
# Each step receives the previous step's output as context

Pros: Output quality improves as context accumulates.

Cons: Slowest option — each step must complete before the next starts.

Pattern 3: Delegation

When to use: A lead agent needs to route subtasks to specialists.

Example: A PM agent delegates research, analysis, and writing to specialists.

from attune.agents.sdk import SDKAgent, SDKAgentTeam

lead = SDKAgent(
    name="lead",
    role="Project coordinator who breaks down tasks and delegates to specialists",
)

research = SDKAgent(name="researcher", role="Technical researcher")
analyst = SDKAgent(name="analyst", role="Data analyst who interprets findings")
writer = SDKAgent(name="writer", role="Technical writer who produces reports")

team = SDKAgentTeam(
    agents=[lead, research, analyst, writer],
    strategy="delegation",
    lead_agent="lead",
)

result = await team.execute("Produce a technical report on our API performance.")

Pros: Flexible — the lead agent adapts task distribution to the specific goal.

Cons: Lead agent quality determines overall output quality.

Pattern 4: Two-Phase (Explore → Synthesize)

When to use: Large, ambiguous tasks where you need to explore before deciding.

Example: Audit a codebase, then produce a consolidated executive summary.

from attune.orchestration import TwoPhaseOrchestrator

# Phase 1: Multiple agents explore in parallel
# Phase 2: A synthesis agent consolidates their findings
orchestrator = TwoPhaseOrchestrator(
    explorers=["security", "code-review", "perf-audit"],
    synthesizer="research",  # Synthesis agent
)

result = await orchestrator.execute({"path": "src/"})
print(result.summary)   # Consolidated view
print(result.raw)       # Per-agent findings

Pros: Best of both worlds — breadth from parallel exploration, coherence from synthesis.

Cons: Two full rounds of LLM calls — higher cost.

Pattern 5: Quality-Gated Retry

When to use: Outputs must meet a minimum quality bar to be usable.

Example: Generated tests must have >80% coverage before accepting.

from attune.agents.sdk import SDKAgentTeam, QualityGate
import subprocess

def coverage_gate(result: str) -> bool:
    """Run the generated tests and check coverage."""
    # Save tests, run pytest --cov, parse output
    coverage = run_tests_and_get_coverage(result)
    return coverage >= 80.0

team = SDKAgentTeam(
    agents=[test_gen_agent],
    strategy="sequential",
    quality_gate=QualityGate(
        check=coverage_gate,
        max_retries=3,
        feedback_prompt="The generated tests had {coverage}% coverage. "
                        "Add more edge cases and error path tests.",
    ),
)

result = await team.execute({"path": "src/auth.py"})

On each failure, the gate feeds structured feedback back into the agent, guiding it toward better output.

Pros: Guaranteed minimum quality; auto-improves with feedback.

Cons: Can be slow if retries are needed; feedback design matters.

Pattern 6: Escalation Chain

When to use: Try a cheap model first; escalate to premium only if it fails.

Example: Use Haiku for simple reviews, escalate to Opus for complex security findings.

from attune.workflows.escalation import EscalationChain, ConfidenceValidator

chain = EscalationChain(
    tiers=["cheap", "capable", "premium"],
    validator=ConfidenceValidator(threshold=0.85),
    max_escalations=2,
)

result = await chain.execute(
    prompt="Analyze this authentication module for security vulnerabilities.",
    context={"code": open("src/auth.py").read()},
)

print(f"Tier used: {result.tier_used}")
print(f"Escalations: {result.escalations}")
print(result.output)

This is Attune AI's EscalationChain — added in v3.6.4.

Pros: Optimal cost/quality tradeoff; only pays for premium when needed.

Cons: More complex to configure; latency can spike on escalation.

Choosing the Right Pattern

Task Type	Recommended Pattern
Independent parallel analysis	Parallel Execution
Step-by-step pipeline	Sequential
Unknown scope, complex routing	Delegation
Broad exploration + summary	Two-Phase
Quality-critical output	Quality-Gated Retry
Cost optimization critical	Escalation Chain

Composing Patterns

Patterns can be nested. A common production setup:

Two-Phase (outer)
  ├── Phase 1: Parallel (3 specialist agents)
  └── Phase 2: Sequential (synthesis → quality gate)

Attune AI's WorkflowComposer handles this nesting:

from attune.orchestration import WorkflowComposer

composer = WorkflowComposer()
result = await composer.execute({
    "goal": "Produce a release-ready audit report",
    "path": "src/",
})
# Composer selects and nests patterns automatically

Further Reading

• Building Agent Teams with the Anthropic SDK
• How to Build AI Agents with Claude
• Agent templates reference
• EscalationChain API