TECHNIQUE
Agentic Orchestration
Across the cited deployed teardowns, multi-agent topologies are mostly built as specialist-role systems: operators split tasks into subagents/nodes, add routing or sequencing, and surround the topology with tool access, tracing, and validation layers.
Decompose the workflow into specialized agents, roles, or graph nodes instead of one monolithic agent.
11 of 11 operators with cited teardown evidence in this pool.Put a router, classifier, director, supervisor, or master agent in front of specialists to choose the next subagent or expert path.
6 of 11 operators with cited teardown evidence in this pool.Chain agent work in stages so one step’s output becomes the next step’s input.
6 of 11 operators with cited teardown evidence in this pool.Use parallel or concurrent delegation where the topology benefits from multiple simultaneous checks or subtasks.
3 of 11 operators with cited teardown evidence in this pool.Add an explicit validation role or falsification step before trusting agent findings.
4 of 11 operators with cited teardown evidence in this pool.Scope context by domain, specialist, or task so agents do not load every tool, rule, or record into one prompt.
5 of 11 operators with cited teardown evidence in this pool.Expose enterprise systems, repositories, data stores, or APIs to agents through tool interfaces, MCP servers, gateways, or similar controlled access layers.
7 of 11 operators with cited teardown evidence in this pool.Instrument multi-agent workflows with tracing, run logs, span visibility, or production monitoring.
6 of 11 operators with cited teardown evidence in this pool.Use different model/cost levels for different agent roles or stages.
2 of 11 operators with cited teardown evidence in this pool.Every operator with cited topology evidence decomposes work into multiple specialized agents, roles, or nodes.
Control-plane topology differs by operator: some use a central supervisor/director, some use staged pipelines or graphs, some expose an agent mesh, and some use a bundle of narrow micro-agents.
APPROACH 01
Central supervisor/director/router selects specialized agents or experts.
APPROACH 02
Staged pipeline or graph where outputs from one agent step feed later steps.
APPROACH 03
Agent mesh/platform where agents call other agents, tools, and systems.
APPROACH 04
Specialized micro-agents with narrow scopes for one product workflow.
Validation differs: operators use different mechanisms to keep multi-agent outputs usable and trustworthy.
APPROACH 01
Critic, disprove-it, adversarial, or grounded-execution validation inside the agent workflow.
APPROACH 02
Tracing, eval sets, offline testing, or production monitoring to iterate on prompts and topology behavior.
Tool and execution integration differs across deployments.
APPROACH 01
Central MCP server or gateway mediates tool/data access and policy enforcement.
APPROACH 02
Tools and subagents are exposed through tool loops, schemas, or shared tool interfaces.
APPROACH 03
Agents execute in sandboxed or isolated environments for code, data normalization, or security testing.
Reliability, noisy outputs, false positives, and hallucination risk remain explicit operator concerns.
Context size, token consumption, latency, and cost require scoping, pruning, caching, or staged model allocation.
Debugging and provenance across agent hops are production concerns; operators add traces, invocation visibility, registries, and audit trails.
Consequential actions still have explicit control boundaries such as human approval, routing to human staff, or gateway policy enforcement.
| Name | Kind | When | Maturity |
|---|---|---|---|
| LangGraph multi-agent graphs | library | explicit, debuggable topology with checkpointing beats emergent chat | established |
| CrewAI | library | role-based agent teams assembled quickly from templates | emerging |
| AutoGen | library | research-flavored conversational multi-agent experiments | emerging |