Choosing Between Agents and Graphs for Dynamic Decision-Making

When designing a system that requires dynamic, adaptive decision-making, the choice between using an agent (e.g., LangChain or OpenAI-style tool-using agent) versus a graph (e.g., LangGraph) depends on several factors.

✅ Use a Graph When:

1. You Have a Known Workflow or Flowchart

Tasks follow a sequence with branching logic.
You can map the decision flow in advance.
Example: Form-filling automation, signup workflows, scraping + verification.

2. You Want Determinism and Visibility

You want full control and observability over flow.
Easier to test and debug individual transitions.

3. You Need Persistent State Across Steps

Complex workflows benefit from reliable state propagation.

4. You Need Recovery / Resumability

Graphs are better at checkpointing and restarting flows.

✅ Use an Agent When:

1. You Want Flexibility and Autonomy

The agent can decide which tool to call and when.
Ideal for open-ended or exploratory tasks.
Example: "Research this topic and summarize with citations."

2. You Have Many Possible Tools/Functions

Agents are better suited for tool selection in large toolsets.

3. Tasks are Not Easily Modeled in a Flowchart

When you can’t predefine a sequence of steps.

4. The Environment is Unpredictable

Agents handle unexpected results more gracefully.

🧠 Hybrid Approach (Agent Inside a Graph Node)

Combine both: use a graph for high-level flow, embed an agent for sub-decisions.
Example: In a "research" node, spawn an agent to decide how to search, summarize, and cite.

Summary Table

Criteria	Use Graph	Use Agent
Known workflow	✅ Yes	❌ No
Open-ended reasoning	❌ No	✅ Yes
Many tool choices	❌ Harder	✅ Easier
Determinism/debuggability	✅ Easy to trace	❌ Harder to trace
Recovery/resumability	✅ Built-in	❌ Manual or custom
Dynamic control flow	⚠️ With effort	✅ Natural fit

🧩 Managing Shared State in Multi-Agent Graphs

When multiple agents or nodes update shared state, it's important to avoid conflicts and maintain integrity. Here are strategies to manage this:

Scoped Namespaces Give each agent its own state namespace, e.g.:

state["agents"]["extractor"]["results"] = ...
state["agents"]["validator"]["status"] = ...

Reduces the chance of overwriting values from other agents.

Immutable Update Pattern Agents return updates, and the graph merges them explicitly.

Avoid in-place mutation:

return { "validator_result": ..., "log": state["log"] + ["Validated"] }

State Merge Rules Define merge logic for concurrent updates:

e.g., last-write-wins, priority-based merging, or even custom resolution functions.

Conflict-Free Data Structures (CRDTs) For more advanced use cases, use CRDTs or append-only logs to manage updates from multiple agents safely.
Guarded Writes Nodes only write to state if certain conditions are met (e.g., no error flag set, a lock released, etc.).
Graph-Orchestrated Sequencing Use explicit sequencing where only one agent can write to a key at a time, based on graph structure.

decagondev/choices.md