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+Behat and AI
+============
+
+AI coding agents can generate and modify code based on behavioural descriptions.
+Behat provides a reliable way to define, verify, and preserve behaviour in these workflows.
+
+By expressing behaviour as executable scenarios, Behat ensures that AI-generated code
+satisfies a clear and objective contract.
+
+Using Behat as a Behavioural Contract
+-------------------------------------
+
+Behat scenarios define observable behaviour in a precise and executable form:
+
+.. code-block:: gherkin
+
+   Feature: Refund processing
+
+     Scenario: Refunding a paid order
+       Given there is a paid order
+       When I refund the order
+       Then the order should be marked as refunded
+
+     Scenario: Prevent duplicate refunds
+       Given an order has already been refunded
+       When I refund the order
+       Then the refund should be rejected
+
+AI agents can implement features and use Behat to verify that the expected behaviour is satisfied.
+When scenarios pass, the behavioural contract is fulfilled.
+
+Executable and Verifiable Behaviour
+-----------------------------------
+
+Behat scenarios serve as executable specifications. They can be used by AI agents to:
+
+* verify correctness after implementing a feature
+* detect behavioural regressions
+* ensure behaviour remains consistent during refactoring
+
+Because scenarios are executable, they remain accurate as the system evolves.
+
+Generating Scenarios with AI
+----------------------------
+
+AI agents can generate Behat scenarios from behavioural descriptions.
+These scenarios can then be reviewed, refined, and executed.
+
+This allows behaviour to be defined before or alongside implementation.
+
+Behat provides a structured and executable representation of behaviour
+that can be used directly by AI tooling.
+
+Human Review and Iteration
+--------------------------
+
+As with any specification or testing tool, generated scenarios and assertions should be reviewed carefully.
+It is critical to ensure that both the human-readable scenarios and the executable step definitions
+accurately represent the intended behaviour. They also need to be robust enough to prove that
+the actual behaviour matches the expectations, even if the implementation changes.
+
+Once validated, scenarios serve as the authoritative definition of the feature. AI agents can implement
+or modify the system until all scenarios pass.
+
+This creates a reliable feedback loop where behaviour is defined, verified, and preserved.
+
+Maintaining Behaviour Over Time
+-------------------------------
+
+With well-designed scenarios and step definitions, Behat ensures that behaviour remains stable as code changes.
+AI agents can safely modify or refactor code, and Behat will detect any unintended behavioural changes.
+
+As long as scenarios pass, the defined behaviour is preserved.
+
+Behat MCP Server
+----------------
+
+Behat provides an `MCP (Model Context Protocol) server`_ that allows AI agents to interact
+directly with Behat projects.
+
+The MCP server enables agents to discover scenarios, execute Behat,
+and integrate behavioural verification into automated workflows.
+
+.. _`MCP (Model Context Protocol) server`: https://github.com/Behat/mcp-server
diff --git a/index.rst b/index.rst
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--- a/index.rst
+++ b/index.rst
@@ -96,6 +96,7 @@ and many more.
    cookbooks
    releases
    useful_resources
+   behat_and_ai
    community
 
 .. _`Behaviour Driven Development`: https://en.wikipedia.org/wiki/Behavior-driven_development