TECHNIQUE
Agentic Orchestration
Across deployed cases, orchestrator–workers is implemented as task decomposition plus routed specialist agents/tools, with topology and control depth varying by workload.
Decompose a complex task into staged subtasks or workers, then coordinate those steps through an orchestrator, planner, executor, graph, or pipeline.
7 of 7 operators with deployed/pilot evidence.Give workers or subagents narrow responsibilities, prompts, tools, or data scopes rather than exposing all work to one general agent.
7 of 7 operators with deployed/pilot evidence.Attach workers to operational tools, data sources, or enterprise systems so orchestration can retrieve context and act in the target workflow.
7 of 7 operators with deployed/pilot evidence.Add separate quality-control stages such as a critic, grader, LLM judge, confidence scoring, filtering, validation, or human oversight around worker outputs.
5 of 7 operators with deployed/pilot evidence.Use parallel or asynchronous execution when subtasks are independent or when the workflow includes long-running jobs.
3 of 7 operators with deployed/pilot evidence.Keep workflow state, evidence, logs, or metadata outside a single prompt through journals, event streams, graph state, persistent memory, or context passing.
5 of 7 operators with deployed/pilot evidence.Every deployed operator decomposes work into multiple coordinated steps, roles, tools, or subagents instead of relying only on one monolithic prompt.
Every deployed operator connects the orchestration layer to real workflow context: enterprise knowledge, code-review systems, search indexes, internal tools, security data, schedulers, proprietary datasets, or external APIs.
Operators use different orchestration topologies.
APPROACH 01
Hierarchical routing or delegation to specialized subagents/personas.
APPROACH 02
Planner/executor or planner-plus-specialist split.
APPROACH 03
Graph/ReAct workflow executor with nodes, edges, states, and tool-enabled apps.
APPROACH 04
Prompt-chained, multi-stage pipeline with pluggable assistants rather than a named hierarchy of agents.
Operators differ in how they control output quality and risk.
APPROACH 01
Use a critic, grader, LLM judge, confidence score, or filtering stage to review generated work.
APPROACH 02
Use validation, persistent experiment logging, human oversight, compute-budget checks, and predefined guardrails around autonomous execution.
APPROACH 03
Configure guardrails for the worker, with no cited critic or judge stage in the teardown evidence.
Operators differ in how they expose tools to the orchestrator.
APPROACH 01
Route to domain subagents or experts that encapsulate specialized tools and instructions.
APPROACH 02
Collapse retrieval complexity behind one purpose-built tool or a dedicated search agent.
APPROACH 03
Offer plugin/native/community tool frameworks so app builders can add capabilities to agents.
Context overload is a practical limiter in long or tool-heavy orchestrations: Slack reports accumulated message history filling the context window and investigations spanning hundreds of inference requests and megabytes of output; Dropbox says precision in context is critical and calls out trimming tool/retrieval context; Atlassian says 1000+ Jira issues cannot be stuffed into the LLM context.
False positives, hallucinations, and evidence-interpretation variability require downstream checks: Uber reports standalone prompts creating many false-positive code-review comments, while Slack uses a weakly adversarial Critic to mitigate hallucinations and variability in interpreting evidence.
Tool and data access are deliberately bounded for safety: Slack safely exposes only a subset of data sources through the tool-call interface; Dropbox cites granular access controls and security reviews for safe code execution; Meta’s announced data-access work adds rule-based risk controls and output guardrails.
Some autonomous workflows remain bounded by oversight or guardrails: Meta keeps human oversight at key strategic decision points and checks compute budgets up front; Build.inc describes agent “training” as giving the agent correct guardrails in a JSON file.
| Name | Kind | When | Maturity |
|---|---|---|---|
| LangGraph supervisor topology | library | a planner delegates to specialized workers with shared graph state | established |
| Temporal workflows | service | orchestration needs durable execution, signals, and replay semantics | established |
| Claude Agent SDK | library | orchestrator-worker agents with tool use and session management out of the box | emerging |