AGNTCon + MCPCon Japan 2026

From a green software perspective, waste in agentic AI comes not only from model choice, but also from workflow design. Agent workflows combine planning, retrieval, retries, memory, tools, and model calls. Poor controls can lead to large models for simple tasks, long histories, repeated retrieval, or needless tool calls, increasing compute, cost, and latency.

This talk presents a design-review approach for reducing unnecessary computation in agent workflows. As a case study, it uses Lean Agentic AI (https://github.com/navveenb/lean-agentic-ai) as an open-source workflow example. For each workflow step, it asks: Is an LLM needed? Is the model appropriate? Is context bounded? Could code, rules, or a smaller model replace a large-model call?

The goal is not precise carbon accounting. Instead, it identifies avoidable inference, oversized context, and needless tool calls before estimating CO2 emissions.

Takeaways:
* See how agent workflow design affects compute, cost, and latency.
* Understand how reducing unnecessary computation supports green software goals.
* Ask simple review questions to reduce unnecessary LLM calls, retrieval, memory, and tool use.