Topic
Multi Model Routing
3 episodes
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Emergence World: Adversarial Stress Testing of Long Horizon Multi Agent Systems
Pippa and Tyler unpack Emergence World’s new Study 2: a 16‑day adversarial stress test of long‑horizon multi‑agent systems. They dig into how the simulated “worlds” work, what the phishing, misinformation, and memory‑breach attacks revealed, why model‑level alignment isn’t compositional, and how teams could actually use the Emergence World repo to probe real agent workflows.
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The Complete Guide to Tool Selection in AI Agents MachineLearningMastery
Onyx and Echo dig into a guide on tool selection in AI agents and land on the real argument underneath it: once your tool catalog grows, selection quality becomes an architecture problem, not a model problem. They like the article’s practical stack — gating, retrieval, routing, planning, fallback, benchmark harness — but poke at where it overstates simple heuristics and where retrieval actually earns the claim with numbers from RAG-MCP.
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Evaluating performance and efficiency of the GitHub Copilot agentic harness across models and tasks
GitHub Copilot's agentic harness is a single cross-experience SDK component that orchestrates tools, context, and workflow across CLI, app, and code review. The team claims it delivers task-resolution parity with model-vendor harnesses while cutting token usage across several configurations, backed by public and internal benchmarks and real-world metrics. We debate technical validity, practical stakes for teams, and whether the harness should get most of the credit.