What breaks when multi-agent tools move into production
The same workflow was built three ways: with , with CrewAI, and directly on Google’s A2A approach. The workflow searched for information, summarized it, and sent a notification. gave the most control over saved state and retry rules, but it was the hardest for new team members to learn.
CrewAI produced a working prototype the fastest, but it became awkward when the workflow needed unusual control steps. Using A2A directly took the most work at the start, but it made failures easier to inspect because the team could see more clearly what was actually sent between systems. failures were often not clean s; one AI agent could silently receive the wrong meaning from another.
None of the three choices solved across agent handoffs by default. Each tool logged its own activity, but none gave one trace from the user’s request through each agent step to the final answer.
Key points
- gives strong control over state and retries, but it takes more learning.
- CrewAI is fast for , but less comfortable for unusual .
- Direct A2A needs more setup, but gives clearer low-level .
- failures can happen quietly when one AI agent misunderstands another.
- End-to-end and trace data are still gaps teams must plan for.