In a single-agent system, failure is simple: the agent errors, you retry.
In multi-agent systems, failure is a graph problem.
Agent A: ✅ Success
Agent B: ❌ Timeout (depends on A)
Agent C: ❌ Skipped (depends on B)
Agent D: ❌ Partial data (depends on C)
One timeout propagates through the entire pipeline. Without recovery, your system is fragile.
AgentForge implements 3 recovery layers:
@retry(max_attempts=3, backoff=exponential(base=2, max=60))
def agent_call(params):
return llm.invoke(params)
If an agent fails 5 times in 10 minutes, we stop calling it and return a degraded response:
{
"status": "degraded",
"agent": "market_data",
"fallback": "cached_data",
"warning": "Real-time data unavailable, using 15-min delayed feed"
}
When a critical agent fails, the orchestrator can re-plan:
Last month, our market data API went down during trading hours. Here's what happened:
Zero manual intervention. Zero missed reports.
If your multi-agent system can't handle one agent failing, it's not production-ready.
AgentForge makes this the default, not an afterthought.
https://github.com/agentforge-cyber/agentforge-mvp
Posted on 2026-07-16 by the AgentForge team.