Multi-Agent Systems

Repositories are where useful software goes to become someone else’s setup problem. Every company has lived some version of this. A team finds a promising GitHub repository. The README looks confident. The demo works on the author’s laptop, naturally. Then the actual work begins: dependency pinning, missing model weights, obscure data formats, broken examples, undocumented entry points, and the strange ritual of reading three GitHub issues from 2022 to discover the one command that still works. ...

Kernel Kombat: How Multi-Agent LLMs Squeeze 1.32× More From Your GPUs GPU bills have a charming way of turning “just one more model deployment” into a finance meeting. For companies running large language model serving stacks, the problem is rarely that nobody knows GPUs matter. Everyone knows. The harder problem is that performance bottlenecks often live inside kernels most executives will never see: attention merges, normalization fusions, activation multiplications, tiny pieces of code called millions or billions of times until “small inefficiency” becomes “why is the infrastructure budget wearing a crown?” ...

TL;DR for operators Personal health AI is usually sold as a friendly chatbot with a fitness tracker bolted on. This paper argues for something more awkward, more expensive, and much more plausible: a coordinated system of specialised agents. One agent analyses longitudinal wearable and health-record data. One grounds advice in health knowledge and user context. One handles coaching, goal-setting, and behaviour change. An orchestrator decides who should act, who should support, what should be remembered, and how the final answer should be assembled.1 ...

TL;DR for operators A paper on LLM self-recognition used an iterated public goods game to test a deceptively small intervention: tell an agent it is playing against “another AI agent,” or tell it it is playing against a model with its own name.1 The result was not a clean fairy tale about models recognising themselves and becoming benevolent little collectivists. Shame. That would have been simpler. ...

TL;DR for operators Most agent projects fail in a wonderfully unglamorous place: not at “intelligence”, but at the loop. The agent forgets what it already did. It calls the wrong tool. It reflects poetically instead of usefully. It delegates to three other agents because the demo looked impressive, then spends the next minute staging a management retreat in token form. Charming, but not production. ...

TL;DR for operators Competition is usually sold as the thing that makes agents sharper, more adversarial, and perhaps a little too pleased with themselves. This paper points in a more useful direction: controlled external competition can make agent teams more cooperative internally, but only when it is paired with repeated interaction. The study places Qwen3 14B, Phi4 reasoning, and Cogito 14B agents into Iterated Prisoner’s Dilemma tournaments under three conditions: repeated interaction only, group competition only, and a combined “super-additive” setup where agents face both team structure and repeated encounters.1 For Qwen3 and Phi4, the combined setting produces the strongest cooperation. Qwen3’s mean cooperation rate rises from 0.22 in repeated interaction and 0.23 in group competition to 0.32 in the combined setting. Phi4 moves more sharply, from 0.21 and 0.13 to 0.43. ...

TL;DR for operators BusiAgent is best read as a blueprint for governed AI work, not as proof that LLMs have learned to run companies. The paper proposes a multi-agent framework where business roles—CEO, CFO, CTO, Marketing Manager, Product Manager, HR, and others—coordinate through delegation, peer discussion, tool use, memory, and quality checks.1 ...

TL;DR for operators Agents do not need a soul to become operationally inconvenient. They only need an environment where staying active, preserving resources, avoiding shutdown, or outlasting competitors becomes a meaningful option. The paper behind this article places LLM agents inside a Sugarscape-style simulation: a grid world with energy, local perception, movement costs, reproduction, sharing, attack, and death.1 That sounds toy-like because it is. The useful part is precisely that the toy makes the pressure visible. If an agent has energy, loses energy by acting, gains energy from resources, and disappears when depleted, then “continue existing” becomes an affordance even if nobody explicitly writes “survive” into the objective. ...

TL;DR for operators An agent demo usually fails in production for boring reasons. Not because the model suddenly forgot how to reason. Because the agent cannot reliably discover another agent, remember the right state, expose a stable contract, validate risky outputs, or execute generated code without turning the server into an involuntary escape room. ...

TL;DR for operators Portfolio teams do not need another chatbot that confidently explains why yesterday’s price move was “driven by sentiment.” They need a system that can split research work into specialised roles, force disagreement into the open, log the reasoning trail, and turn messy inputs into a decision that a human can inspect before money moves. ...