crewAIInc / crewAIInc/crewAI

[FEATURE] Add pre-execution validation for agent-to-agent actions

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Dominant language
Python
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Description

Feature Area

Other (please specify in additional context)

Is your feature request related to a an existing bug? Please link it here.

no

Describe the solution you'd like

CrewAI enables multi-agent workflows where agents collaborate and trigger actions across tasks and tools.

In these systems, actions can propagate between agents before ultimately resulting in execution (tool calls, API requests, etc).

It would be valuable to have a deterministic validation step at the moment of execution to ensure consistency and correctness of actions across agent interactions.

In multi-agent workflows:

  • actions generated by one agent are passed to another
  • parameters may evolve across interactions
  • execution depends on the final payload produced at runtime

Adding validation at the execution boundary would help ensure that the final action being executed is complete, expected, and consistent with system constraints.

Suggested approach

Introduce a validation step before execution that verifies:

  • originating agent and receiving agent
  • action being performed
  • parameters (schema + expected values)
  • destination or external system
  • timestamp / validity window
  • optional nonce or replay protection

If validation fails → execution does not occur.

This could be implemented as a wrapper around execution functions or as a system-level layer to ensure consistent behavior across agent interactions.

Describe alternatives you've considered

Validation can be implemented within individual tasks or agents, but this makes enforcement optional and inconsistent across workflows.

A system-level validation layer would ensure consistent behavior regardless of how individual agents or tasks are configured.

Additional context

As multi-agent workflows become more complex, actions can pass through multiple layers before execution.

Having a consistent validation step at the execution boundary would help ensure predictable and reliable behavior across all agent interactions.

Willingness to Contribute

Yes, I'd be happy to submit a pull request

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

The issue names no files or tests; start by locating the execution functions and the task or agent boundaries where tool calls and API requests are dispatched. Define the validation layer's required action fields and ensure invalid actions prevent execution across multi-agent workflows, with coverage for the stated validation inputs and replay controls.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
ai
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Active
Clarity
Needs clarification
Newbie friendliness
30/100

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