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Agentic AI
Planning & Control Flow
Agentic Behavior Tree
Borrow the behavior-tree formalism — leaves are LLM calls or tools that return success/failure; a tree of selectors and sequences orchestrates control flow.
Intent & Description
🎯 Intent
Borrow the behavior-tree formalism: leaves are LLM calls or tools that return success/failure; a tree of selectors and sequences orchestrates control flow.
📋 Context
An agent needs structured orchestration with clear fallback semantics — try one approach; if it fails, try the next; if all fail, escalate. Pure prompt chains and free-form ReAct loops have no first-class concept of “failure of a sub-task triggers the sibling branch”. Behavior trees, widely used in game design and robotics, are the canonical formalism for this shape.
💡 Solution
- Build the agent as a tree of nodes. - Interior nodes are Selectors (try children left-to-right, succeed on first success) and Sequences (run children left-to-right, fail on first failure), plus standard decorators (Retry, Timeout, Invert). - Leaves call the LLM or a tool and return SUCCESS or FAILURE. - The tree executes top-down per tick; status propagates upward. - The tree itself is a versioned artifact that reviewers can read and diff.
Real-world Use Case
- Control flow has structured retries, fallbacks, and escalations.
- Reviewing the agent’s structure is a first-class need.
- Multiple leaf implementations (LLM, tool, sub-agent) need uniform success/failure semantics.
Source
Advantages
- Retry, fallback, and escalation are first-class structural choices.
- Reviewable as a tree, not a prompt.
- Composes naturally with sub-agents at leaves.
Disadvantages
- Tree authoring is up-front design work; ad-hoc cases want to bypass the tree.
- Mixing LLM leaves with deterministic ones complicates timing and cost reasoning.
- Authors may overuse decorators to paper over leaf flakiness.