How can an agent execute independent tasks in parallel?

An AI agent can execute independent tasks in parallel by starting multiple steps concurrently when none needs the output of another. The workflow waits for the required results and then combines them before continuing.

Example: Preparing a customer summary

After obtaining a customer ID, the agent needs three kinds of information:

• Recent orders
• Support tickets
• Account usage

All three tools need the same customer ID, but none needs another tool's result.

Instead of calling them one after another, the agent can launch the three requests together. Once the results arrive, it generates the summary.

Fan-out: Start independent tasks.

Fan-in: Collect their results at a join step.

How does the agent do this?

1. Identify dependencies. Check each step's required inputs. A task is ready only when its prerequisites are satisfied.
2. Select ready tasks. Find steps that can proceed without waiting for one another.
3. Launch concurrently. An orchestrator starts those tasks using asynchronous calls, parallel workflow branches, or separate workers, depending on the runtime.
4. Track each result. Save outputs and failures against their corresponding task IDs so results are not mixed up.
5. Join and validate. Wait for the results needed by the next step, check their validity, and merge them into workflow state.
6. Continue or recover. If a required branch fails, retry that branch, use an approved fallback, or report partial completion as appropriate.

Parallel does not always mean faster

It helps most when tasks spend time waiting on separate APIs or services. Gains may shrink when tasks compete for the same bottleneck.

A practical implementation should enforce:

• Concurrency limits: Avoid launching more work than services or workers can handle.
• Rate limits and budgets: Respect API quotas, token usage, and execution costs.
• State isolation: Keep branch-specific intermediate results separate; merge them using defined rules.
• Side-effect safety: Do not concurrently update the same record unless the workflow handles conflicts.
• Timeouts and failures: Decide whether the next step requires every result or can proceed with an explicitly allowed partial result.

When should tasks remain sequential?

If step B needs a value produced by step A, they are not independent.

For example:

Find customer → obtain customer ID → fetch orders

The customer lookup must finish first. But once the ID is known, several independent lookups can run concurrently.

In short

Parallel execution means running dependency-free agent steps concurrently, then safely joining their outputs. It reduces unnecessary waiting while preserving the ordering, state consistency, and authorization requirements of the overall plan.