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Agentic AI

Let AI agents delegate what they can't do alone

Subagentic delegation is a design pattern where a supervisor agent decomposes a complex goal and routes each subtask to a specialized subagent — running in parallel, at scale.

Learn More See the pattern
Tasks run in parallel, not sequenced through a single model
Each subagent carries only the context it needs
Supervisor synthesizes outputs into one coherent result
Supervisor Agent
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Research · Code · Validate
Core Concepts

How subagentic delegation works

Three moving parts. One coordinated result.

Supervisor Agent
Receives the high-level goal, breaks it into discrete subtasks, and routes each to the right specialist. It never executes — it coordinates.
Specialized Subagents
Purpose-built agents handle narrow domains — research, code generation, validation, writing — each with focused context, tools, and prompting.
Result Aggregation
Subagent outputs return to the supervisor, which validates, merges, and synthesizes them into a single coherent final result.
0 x
Faster on complex tasks vs single-agent
0 %
Less context pressure per subagent
0 +
Subagents typical in production orchestrations
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Parallel execution across independent subtasks
FAQ

Common questions

What is subagentic delegation? +
A design pattern where a primary AI agent (the supervisor) decomposes a complex goal into subtasks and delegates each to a specialized subagent. The supervisor coordinates the workflow; subagents execute their slice and return structured outputs.
When should I use this pattern vs a single agent? +
Use delegation when tasks exceed a single context window, require parallel workstreams, or benefit from specialized tools and prompting per domain. A single agent is fine for narrowly scoped, sequential tasks where overhead isn't justified.
Which frameworks support subagentic delegation? +
LangGraph, AutoGen, CrewAI, and the Claude Agent SDK all support multi-agent orchestration natively. Most modern agentic frameworks expose agent handoffs or tool-calling subagents as first-class primitives.
What are the main failure modes to watch for? +
Over-delegation (too many hops add latency and cost), context loss at handoff boundaries, and subagent error propagation. Mitigate with clear task schemas, structured outputs, and validation checkpoints before aggregation.
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