Hamish Songsmith

Does This Agent Make My Context Look Big? Right-Sizing AI Architectures for Production

All-in-one personal agent harnesses showcase the incredible potential of capability-rich AI assistants. But deploying a monolithic "do-it-all" agent into production often leaves teams struggling with context dilution, fragile tool calls, un-evaluable execution paths, and massive security blast radiuses.

However, swinging to the opposite extreme—decomposing every task into a complex micro-agent graph—swaps prompt engineering problems for software engineering complexity. Inter-agent handoffs are inherently lossy, graph state deadlocks happen, and you lose the "free capability boost" of swapping in new foundation models overnight.

In this session, we will unpack the art of Right-Sizing AI Architectures. We will examine how to use soft-deterministic micro-agents where they yield the highest ROI (like context-heavy routing and non-LLM ground-truth evals), while avoiding the unnecessary overhead of over-engineered agent networks. Attendees will leave with a practical decision framework for matching agent boundaries to actual production requirements.