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.

A production issue spans multiple cloud environments. Everything looks fine in isolation, but the system behaves unexpectedly end-to-end. Reconstructing what is actually deployed requires stitching together cloud consoles, infrastructure definitions, and internal tooling.

AI agents could help with this kind of problem, but they need better context. Infrastructure is a key part of that context gap. Cloud resources, environments, configurations, and policies are essential for reasoning and decision-making, yet this information is fragmented across infrastructure-as-code, cloud providers, and internal systems.

This talk explores why a developer platform was built: to unify infrastructure management, standardize how teams provision and manage cloud resources, and provide a consistent abstraction across providers, accounts, and environments. The platform then evolves into a unified source of infrastructure context, aggregating metadata into a coherent infrastructure blueprint. By exposing this through an MCP server, it allows AI agents to understand the full infrastructure landscape, improving reasoning, troubleshooting, and the quality of assistance in engineering workflows.