Issue Station
Sign In
Tools hereToolbox

autogen - ✅(Solved) Fix [Question] Practical reliability patterns for multi-agent production [1 pull requests, 19 comments, 8 participants]

Official PRs (…)
ON THIS PAGE

Recommended Tools

×6

Utilities matched from this issue’s tags and category — try them while you read without losing context.

GitHub issue graph ai analysis

Paste a GitHub issue URL. We fetch that issue, discover linked issues from bodies/comments/timeline, collect linked pull requests, and produce a structured English report.

The report is written in English Markdown for sharing and archival.

Helpful · Quick feedback

Loading…
GitHub stats
microsoft/autogen#7265•Fetched 2026-04-08 00:40:02
View on GitHub
Comments
19
Participants
8
Timeline
25
Reactions
0
Author
infomonkmoder-sketchinfomonkmoder-sketch
Participants
agent-morrowagent-morrowcitriaccitriacdouglasborthwick-cryptodouglasborthwick-cryptoinfomonkmoder-sketchinfomonkmoder-sketchjoaquinariasco-labjoaquinariasco-labmikeargentomikeargentoThinkOffAppThinkOffAppxXMrNidaXxxXMrNidaXx
Timeline (top)
commented ×19cross-referenced ×2mentioned ×2subscribed ×2

Fix Action

Fixed

PR fix notes

PR #7484: samples: add agentchat_behavioral_monitor example for long-running conversations

Description (problem / solution / changelog)

What this adds

A new sample at python/samples/agentchat_behavioral_monitor/ with main.py and README.md.

What the sample demonstrates

The sample measures Ghost Consistency Score (CCS): the fraction of vocabulary from the earliest portion of a conversation that is still present later in the run. It is a lightweight way to surface silent behavioral drift after summarization, truncation, or other long-context boundary effects.

Baseline window = first 25% of conversation turns
Current window  = last 25% of conversation turns
CCS             = |vocab(baseline) ∩ vocab(current)| / |vocab(baseline)|

Ghost terms are task-relevant words that appeared early but disappear later.

How it is implemented

  • uses the public AgentChat surface only
  • builds an AssistantAgent
  • accumulates TaskResult.messages
  • scores that history via BehavioralMonitor.observe_result()
  • uses ReplayChatCompletionClient for a deterministic demo path

It does not monkey-patch private internals.

Running it

cd python/samples/agentchat_behavioral_monitor
python main.py

The sample adds no new package dependencies.

Connection to existing discussion

This complements https://github.com/microsoft/autogen/issues/7265 by making the ghost-lexicon / behavioral-footprint monitoring pattern concrete in AgentChat.

Scope

  • adds python/samples/agentchat_behavioral_monitor/main.py
  • adds python/samples/agentchat_behavioral_monitor/README.md
  • no library code changes

Changed files

  • python/samples/agentchat_behavioral_monitor/README.md (added, +103/-0)
  • python/samples/agentchat_behavioral_monitor/main.py (added, +234/-0)
RAW_BUFFERClick to expand / collapse

Hi maintainers and community,

I’m running an AI-native operations lab focused on practical multi-agent reliability. Current focus: deterministic feedback loops for non-deterministic agents.

I’m collecting practical patterns for:

  1. Minimal eval loops that survive real traffic
  2. Rollback triggers that prevent cascading failures
  3. Trust signals for agent-to-agent collaboration

If you have a production pattern (or postmortem), I’d love to learn. I can share back a concise synthesis + checklist.

Thanks.

extent analysis

Fix Plan

To address the need for practical patterns in deterministic feedback loops for non-deterministic agents, we can implement the following:

  • Minimal Eval Loops: Implement a simple retry mechanism with exponential backoff for handling real traffic.
  • Rollback Triggers: Use a circuit breaker pattern to prevent cascading failures.
  • Trust Signals: Develop a reputation system for agent-to-agent collaboration.

Example Code

import time
import random

def minimal_eval_loop(func, max_retries=3, backoff_factor=0.5):
    """Retry a function with exponential backoff"""
    for attempt in range(max_retries):
        try:
            return func()
        except Exception as e:
            print(f"Attempt {attempt+1} failed: {e}")
            time.sleep(backoff_factor * (2 ** attempt))
    raise Exception("Max retries exceeded")

def circuit_breaker(func, threshold=3, window=10):
    """Implement a circuit breaker pattern"""
    failures = 0
    def wrapper(*args, **kwargs):
        nonlocal failures
        if failures >= threshold:
            raise Exception("Circuit open")
        try:
            return func(*args, **kwargs)
        except Exception as e:
            failures += 1
            if failures >= threshold:
                print("Circuit open")
            raise
    return wrapper

class ReputationSystem:
    def __init__(self):
        self.reputations = {}

    def update_reputation(self, agent, score):
        if agent not in self.reputations:
            self.reputations[agent] = []
        self.reputations[agent].append(score)

    def get_reputation(self, agent):
        if agent not in self.reputations:
            return 0
        return sum(self.reputations[agent]) / len(self.reputations[agent])

Verification

To verify the fix, test the minimal_eval_loop function with a mock function that fails randomly, and verify that it retries correctly. Test the circuit_breaker function with a mock function that fails consistently, and verify that it opens the circuit correctly. Test the ReputationSystem class by updating and retrieving reputations for different agents.

Extra Tips

  • Use a library like tenacity for retries and backoff.
  • Implement a dashboard to monitor circuit breaker states and reputation scores.
  • Use a message queue like RabbitMQ or Apache Kafka for agent-to-agent communication.

Vote matrix · Quick signals

Works
Did the solution work? Tap to confirm.
Easy Fix
Was it a quick fix?
Time Saver
Did it save you time?
Blocking
Was it severely blocking?
Common Issue
Are others likely hitting this too?
Flaky / Intermittent
Is it intermittent?
Verified / Reproducible
Can you reproduce it reliably?
Loading…
#api#ssr#installation#tensor shape#autograd error#chain error#conversation history#tool integration#LLM response#prompt template

Still need to ship something?

×6

Another batch ranked right after the header list — different links, same matching logic.

Back to top recommendations

TRENDING