TECHNIQUE
Agentic Orchestration
Observed deployments use multi-agent topologies as routed, role-specialized workflows: supervisors/directors/classifiers/graphs/trees/meshes delegate to subagents, with explicit context, tooling, and verification controls.
Operators compose work across multiple agents, nodes, or agent-to-agent workflows instead of relying on one monolithic prompt.
11 of 11 operators in the teardown pool show multi-agent or multi-node orchestration evidence.A routing or coordinating layer chooses which subagent, expert, branch, node, or downstream agent should handle the next step.
9 of 11 operators show an explicit coordinator, router, graph, branch, classifier, director, supervisor, or mesh coordination layer.Operators specialize agents by role or domain: examples include red/blue teams, Director/Expert/Critic teams, read/RAG/action agents, Jira agents, reviewer/scout agents, and micro-agents for security, duplication, or editorial checks.
9 of 11 operators show role- or domain-specialized agents.Operators connect agents to tools or enterprise data systems, including MCP/tool-call interfaces, code/log/query tools, JQL execution, document retrieval, remote VMs, and downstream APIs.
11 of 11 operators show agents or subagents using tools, retrieval systems, data sources, or downstream APIs.Several operators add a separate verification, critique, or falsification stage before trusting findings or making changes.
3 of 11 operators show a dedicated adversarial, critic, blue-team, or falsification step.Many operators instrument the topology with tracing, evaluations, production monitoring, offline eval sets, or acceptance metrics to debug multi-step behavior.
9 of 11 operators show tracing, monitoring, evaluations, or production/debug metrics around the agent topology.Operators manage context pressure by pruning, scoped loading, external working memory, key-value observation stores, batching, or caching.
6 of 11 operators explicitly describe context-management mechanisms for multi-agent workflows.Across the observed pool, the topology is not just multiple prompts: every operator connects agents or nodes to tools, data sources, downstream systems, or retrieval/action interfaces.
Operators differ in the orchestration shape they chose.
APPROACH 01
Supervisor, director, classifier, controller, or lead-scout routes work to subagents.
APPROACH 02
Graph, tree, DAG, or node workflow coordinates execution.
APPROACH 03
Agent mesh/platform supports agents calling other agents, tools, and systems.
Operators specialize agents around different units of work.
APPROACH 01
Security investigation or adversarial testing roles, such as Director/Experts/Critic or red-team/blue-team agents.
APPROACH 02
Enterprise domain or product-surface agents, such as read/RAG/action agents, Jira agents, domain experts, and subagents for complex work queries.
APPROACH 03
Code-review pipelines split scouting, deep review, and narrow review checks across agents.
APPROACH 04
Transaction-workflow agents or branches follow different parts of an operational process.
Operators differ in how much autonomy they allow around writes or production changes.
APPROACH 01
Agents execute or prepare write operations inside product/workflow systems.
APPROACH 02
Agents generate detection-rule improvements, but human oversight approves changes before production deployment.
APPROACH 03
Agents focus on investigation, retrieval, reports, or quality checks rather than cited production writes.
Operators differ in how they keep multi-agent context bounded and inspectable.
APPROACH 01
Scoped loading or pruning reduces what the agent sees before routing or answering.
APPROACH 02
External memory, journals, artifact inspection, or key-value observation retrieval keeps long investigations from relying only on message history.
APPROACH 03
Caching manages overlapping context across specialized micro-agents.
Single-prompt or single-agent designs were reported as struggling with complex workflows, noisy comments, limited context, or too many tools.
Context windows and large intermediate outputs remain a recurring constraint in long-running or data-heavy agent workflows.
Operators report or mitigate hallucinations, false positives, false negatives, and agents taking the wrong path; several add critic, grounding, or falsification stages.
Multi-agent specialization can increase token or cost pressure, so some operators explicitly manage caching, model tiering, or cost metrics.
| 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 |