What is an Agent Graph?
An agent graph is a directed graph representation of an AI agent's workflow, where nodes represent computation steps (LLM calls, tool executions, policy checks) and edges define the flow of data and control between steps.
WHY IT MATTERS
Graph-based agent architectures make complex workflows explicit and debuggable. Instead of opaque agent loops where you hope the LLM figures out the right sequence, graphs define the exact flow — with conditional branching, parallel execution, and cycle support.
LangGraph is the leading implementation, but the concept is broader. Any agent workflow can be modeled as a graph: start → gather data → analyze → decide → execute → verify. Each step is a node, and edges carry state between them.
For financial agents, graph architectures let you insert policy checkpoints at specific points in the workflow. Before the 'execute transaction' node, add a 'validate against policies' node. The graph makes these control points explicit and guaranteed.
HOW POLICYLAYER USES THIS
PolicyLayer integrates as a node in agent graphs — a policy validation checkpoint that sits between decision and execution nodes. This ensures every financial action passes through policy checks regardless of the workflow path.