Strands AI Functions: Post-Conditions and Multi-Agent Teams as Python Functions

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Post-conditions pattern:

@ai_function(post_conditions=[check_length, check_style], max_attempts=5)
def summarize_meeting(transcripts: str) -> MeetingSummary:
    """Write a summary of the following meeting in less than 50 words."""

Post-conditions can be plain Python assertions or other AI Functions. The function only returns once eve

github.com →

Connections

Supports

COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation Post-condition verification patterns in Strands functions provide a mechanism that skill-distillation frameworks could use to validate generated skills.

Develops into

Strands AI Functions: Python Library for Verified Agent Workflows The base Strands AI Functions library is extended with post-conditions and multi-agent team patterns as decorators.

Building an Advanced Agentic Harness Post-conditions and multi-agent teams as Python functions directly parallels the verification hierarchies and role separation (P-roles) described in the harness construction

Supported by

Introducing Dogwood: runtime verification for AI agents Dogwood's runtime verification of agent behavior directly supports the post-condition verification model described in Strands, where agent actions must satisfy declared contracts

Type:	link
First published:	Monday, 20 July 2026 at 12:00 UTC
Location:	https://github.com/strands-labs/ai-functions
URL:	https://mattwood.fyi/i/7eec841a