Monolithic agents fail at complex tasks. Enterprise systems require multi-agent orchestration, where specialized agents collaborate to achieve a broader goal.
- Hierarchical Orchestration: A Manager agent breaks down a task and delegates sub-tasks to specialized Worker agents.
- Sequential Pipelines: Output from one agent flows deterministically into the next, ideal for data processing.
- Collaborative Debate: Two agents evaluate a solution from different perspectives (e.g., Coder vs. Reviewer) until consensus is reached.
Pattern Comparison
| Pattern | Best For | Failure Mode |
|---|---|---|
| Hierarchical (Manager/Worker) | Open-ended tasks needing dynamic decomposition | Manager over-delegates, creating excessive coordination overhead |
| Sequential Pipeline | Well-defined, ordered workflows (ETL, document processing) | A single stage failure blocks the entire pipeline |
| Collaborative Debate | High-stakes outputs needing quality review (code, contracts) | Agents can loop indefinitely without a forced convergence rule |
| Router/Dispatch | High-volume, heterogeneous requests needing triage | Misclassification sends requests to the wrong specialist agent |
Hierarchical Orchestration in Practice
A Manager agent's job is decomposition and synthesis, not execution. It should never call low-level tools directly — that couples planning to implementation detail and makes the manager's prompt bloat as tools are added. Instead, it delegates to Worker agents, each scoped to a narrow tool set and a single responsibility.
class ManagerAgent:
def __init__(self, workers: dict):
self.workers = workers # {"research": ResearchWorker(), "code": CodeWorker()}
def run(self, task):
plan = self.decompose(task) # LLM call: break task into sub-tasks
results = []
for subtask in plan.steps:
worker = self.workers[subtask.worker_type]
results.append(worker.execute(subtask))
return self.synthesize(task, results) # LLM call: combine into final answerForcing Convergence in Debate Patterns
Collaborative debate (a Coder agent proposes, a Reviewer agent critiques) improves output quality but has no natural stopping point. Without a convergence rule, two agents can iterate indefinitely, each finding a new nitpick to justify another round.
Convergence Rules
- Fixed round cap: Stop after N rounds regardless of outcome, and surface the last proposal with any unresolved critiques attached.
- Severity threshold: Only block on critiques above a defined severity; cosmetic disagreements don't trigger another round.
- Tie-breaker agent: If two rounds fail to converge, escalate to a third agent (or a human) to make the final call.
State Ownership Across Agents
The most common bug in multi-agent systems is ambiguous state ownership — two agents both writing to the same shared context and clobbering each other's updates. Enterprise deployments should assign each piece of state a single writer: the Manager owns the overall plan and task status; each Worker owns only the intermediate artifacts it produces, passed back explicitly rather than mutated in place.