What is agent orchestration?
It is the control model around delegated AI work: classification, gating, worker scoping, validation, and final integration.
AGENT_ORCHESTRATION
Agent orchestration architecture starts with control, not parallelism.
Agent orchestration is the operating model for deciding which AI work should stay in the main session, which work can safely be delegated, and which outputs require validation before they influence code, infrastructure, or public claims.
DIRECT_ANSWER
Agent orchestration is the control system around delegated AI work.
A useful agent orchestration architecture does more than start multiple workers. It classifies the task, checks whether delegation is worth the overhead, limits what each worker can see or change, validates returned work, and keeps final responsibility in a main session. This is the positioning behind AgentFanout: a portable routing and policy layer for worker-capable runtimes, not a separate execution framework.
CONTROL_MODEL
The main session is the authority boundary.
Multi-agent systems fail when every worker receives broad authority. The safer model is to treat the main session as the security boundary, final integrator, and owner of irreversible decisions. AgentFanout uses that model explicitly: private tools, credentials, git state, destructive operations, and final synthesis stay in the main session while delegated workers receive narrow packets.
This distinction matters for enterprise-style AI work because real tasks often cross boundaries: repository state, infrastructure details, public documentation, private credentials, and deployment surfaces. Orchestration is the discipline of deciding which boundaries cannot move, even when parallelism is available.
| Layer | Responsibility | Failure It Prevents |
|---|---|---|
| Main session | Owns private tools, secrets, git state, destructive actions, and final synthesis. | Unreviewed workers taking irreversible or privileged actions. |
| Router policy | Classifies tasks, applies hard gates, and decides whether fanout is useful. | Launching workers for tiny, coupled, unsafe, or non-independent tasks. |
| Worker packet | Defines one bounded task, allowed context, forbidden actions, and expected output. | Workers duplicating scope, leaking context, or returning unusable synthesis. |
| Validation gate | Checks worker output against source evidence, tests, browser state, or review criteria. | Parallel work being accepted because it sounds plausible rather than because it is correct. |
BOUNDED_DELEGATION
Workers should be scoped like production change requests.
A worker packet should name the task, ownership boundary, allowed sources, forbidden actions, and output contract. It should be small enough to verify. That is the difference between useful fanout and noisy parallel prompting.
Hard gates first
Secrets, private accounts, destructive commands, git publication, and final synthesis are not delegated.
Independent tasks only
Fanout is useful when workers can progress without blocking one another or writing over the same surface.
Validation is part of orchestration
Worker results need tests, source evidence, diff review, or rendered checks before integration.
MARKET_CONTEXT
Frameworks execute workflows. A routing layer decides what should be delegated.
OpenAI's Agents SDK, Claude subagents, and CrewAI all show that multi-agent delegation is now a mainstream development pattern. AgentFanout is deliberately narrower. It does not need to replace those runtimes. It supplies portable policy: when fanout is worth it, how the worker should be shaped, which provider class fits, and what validation must happen before the main session trusts the result.
That narrowness is a strength for teams that use multiple agent environments. A routing policy can travel across Codex, Claude, MiniMax, local LLMs, and future adapters more easily than a full execution framework can replace every host.
IMPLEMENTATION_CHECKLIST
What an orchestration policy should define before workers run.
The implementation standard is intentionally concrete. A worker packet should define the task, write scope, allowed sources, forbidden actions, output format, validation method, and escalation path. Without those fields, parallel work becomes a coordination problem rather than a productivity gain.
- Task classification: research, coding, review, debugging, content, release, or main-session-only work.
- Authority boundary: which tools, credentials, files, and public surfaces are excluded from workers.
- Worker packet shape: objective, context, ownership, expected output, and stop conditions.
- Validator requirement: tests, rendered UI, source citations, diff review, or human approval.
- Integration rule: the main session synthesizes and owns final public claims.
This is why AgentFanout should be described as a control layer. Its value is not that it launches the largest number of workers; its value is deciding when workers are warranted and keeping the dangerous parts of the task out of the delegated scope.
SOURCE_NOTES
Sources used for this page.
FAQ
Agent orchestration questions.
When should orchestration avoid fanout?
When the task is tiny, tightly coupled, credential-sensitive, destructive, or requires final judgment from the main session.
Does AgentFanout execute workers directly?
No. AgentFanout provides routing policy and worker packet shaping; the host runtime launches workers.
What makes orchestration safer?
Clear authority boundaries, narrow worker scope, explicit forbidden actions, and validation before integration.
NEXT_STEP
Use AgentFanout as the routing policy reference.
AgentFanout is the SynapseGrid Labs project that turns these orchestration rules into a portable skill and deterministic advisory router.