As someone deeply invested in agentic systems and reinforcement learning, I find the Chain-of-Agents (CoA) approach both refreshing and practical, it takes the chaos of multi-agent orchestration and distills it into something elegant: a single model with native agent-like behavior. Here’s what I see: Traditional multi-agent setups have been great for modularity and reasoning, but they come with a high tax, too much inter-agent communication, redundant memory syncing, and unnecessary tool calls. What CoA proposes is radical in its simplicity: distill the behaviors of successful multi-agent runs into one model, then train that model to act as a team, internally. They start with multi-agent distillation: successful traces from existing orchestrators like OAgents are transformed into CoA-style traces, complete with planning steps, tool usage, reflections, and execution paths. But they don’t stop at replay, they filter hard cases and focus on high-quality trajectories that demonstrate useful agent behaviors like tool efficiency and coherent planning. This becomes the foundation of their SFT phase. Once that base is established, Agentic RL kicks in. The model is tasked with solving complex reasoning problems and importantly, ones where tools make or break the outcome (web QA, coding, math). Rewarding is done via exact-match or LLM-as-Judge strategies, making it possible to scale reward signals across noisy tasks. This setup tunes the model to reason reflectively, use tools judiciously, and respond with stability under diverse challenges. The kicker: by collapsing the roles of multiple agents into a single coherent model, CoA delivers an 84.6% reduction in inference cost. It’s a major unlock for production-grade agent systems where latency, memory, and tool limits matter. What excites me most is how CoA generalizes the spirit of ReAct/TIR, but upgrades it, dynamically invoking roles, using tools only when needed, and preserving a unified memory state that avoids the “who’s speaking now?” confusion. To me, Chain-of-Agents is a promising new frontier: a single-model, multi-mind architecture that captures the power of coordination while optimizing for real-world deployment constraints. And that’s where agentic AI needs to go. https://lnkd.in/exTxvsDD
Thanks for sharing. I‘d highly recommend this https://www.amazon.de/Introduction-MultiAgent-Systems-Second/dp/0470519460?source=ps-sl-shoppingads-lpcontext&ref_=fplfs&psc=1&smid=A3JWKAKR8XB7XF&language=de_DE many new words are created for concepts that exist for a while now. That said, controls for error aggregation are badly needed. I hope the authors will extend to quantify error margins at some point.
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1moBijit, what stood out to me in the paper is how CoA doesn’t just reduce inference cost, it changes the reliability game. In traditional multi-agent setups so much fragility comes from coordination overhead, context drops, memory desync, redundant tool calls. By distilling those behaviors into one model, CoA shifts that complexity into training instead of leaving it to runtime, which feels like a much more stable foundation. I also found the reward signal angle really interesting. Instead of bolting on orchestration logic to decide when an agent should reflect or act, the model internalizes those behaviors through distillation and reinforcement. It’s less about stitching workflows together and more about teaching the model agent-like judgment. Do you see this approach extending cleanly into domains like robotics, where tool use isn’t just APIs but physical actions with safety and latency constraints? That seems like the next big test for whether this paradigm holds.