Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git...
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Love the banner ๐
Thanks a lot bud <3
What happens after a one-way handoff goes a little fuzzy for me. Once the triage agent passes the whole conversation to the order agent, what does the user do when their next message is actually a billing question? With the manager pattern the boss keeps the thread so that case is easy, but with peer handoffs it feels like you either give every specialist a "send it back to triage" escape hatch or you accept that one wrong route strands the user. Curious how you've seen the return trip handled, or whether you just design so handoffs are rare.
The "max out one agent first" advice matches what I keep relearning. Every time I split too early, the cost showed up not in the agents themselves but in the handoffs, where context quietly dropped between turns and nobody owned the failure. I now only reach for a second agent when one tool set genuinely fights another for the same context window.
I see
The "max out one agent before splitting" advice is underrated. I see too many teams jump straight to multi-agent architectures because the diagrams look impressive, then spend weeks debugging handoff logic that a single agent with well-scoped tools would have handled fine.
Your point about tool overlap being the real breaking point resonates. I have dealt with agents that had 8 tools where 3 of them did similar things with slightly different APIs. The model would pick the wrong one half the time. Splitting by tool domain solved it instantly.
One thing I'd add: the memory question cuts across both patterns. Whether you run one agent or five, they all need to persist context between turns. I have been experimenting with embedded storage for agent state (working on moteDB for this exact reason), and the tradeoff is the same either way. You either keep everything in memory and lose it on restart, or you add a persistence layer and deal with the latency tax.
What is your take on agents that need to share intermediate state? Do you pipe outputs between them directly, or use a shared store that each agent reads from?
Another killer breakdown, brooh!
The advice to "max out a single agent before reaching for many" is a massive lifesaver. Itโs so easy to fall into the trap of over-engineering a massive multi-agent mesh network for something that really just needed a couple of extra tools and a cleaner prompt template. "Debugging at 11pm" is a feeling way too many devs know too well!
Your explanation of the Manager pattern vs. Decentralized Handoffs is incredibly clear. Passing a specialist agent as a tool to the manager is such an elegant design pattern that doesn't get explained simply enough in most documentation.
Thatโs usually where things get really messy in production. Keep up the great work with this series and git-lrc!
Thanks G :)
The orchestration question gets harder when agents need persistent memory. If one agent runs and forgets, you end up re-explaining context every loop. If you split across many agents, how do they share state without creating a coordination mess?
We hit this with moteDB -- an embedded multimodal DB designed for embodied AI scenarios. The core insight was that agents need structured memory that survives the session, not just rolling context windows.
Single-agent with memory, or multi-agent with shared state -- which pattern are you finding works better in practice? The coordination overhead of many agents seems like it could eat the productivity gains.
The one-agent-or-many question usually gets answered backwards, teams reach for many because it looks sophisticated, then drown in coordination overhead. The honest default is the fewest agents that can do the job with clear, checkable handoffs. Multi-agent only pays off when each agent has a tight, verifiable boundary; otherwise you've built a system harder to reason about than the monolith it replaced. Orchestration without enforced contracts between agents is just distributed confusion.
This made me thinkโat what point do you decide it's time to split one agent into multiple specialized agents? Is there a clear rule of thumb?