AI agents are only as useful as the tools they can use. But once agents start acting through real systems, tool choice becomes an architecture decision, a security decision, and a product decision. Should your agent call an API directly? Use an MCP server? Run a CLI command? Drive a browser? Query a database? Ask a human?
This talk presents a practical decision framework for choosing the right execution interface for AI agents. We will compare MCP, direct APIs, CLIs, and browser automation across reliability, safety, permissions, observability, ergonomics, and failure modes.
The core argument: agents should use the most structured, least powerful, most observable interface that can complete the task. Direct APIs are best for stable product workflows. MCP is powerful for standardizing tool access across many clients. CLIs are useful for developer workflows but risky when they expose broad machine-level power. Browser automation is valuable for legacy systems but should often be treated as a fallback.
The goal is to build an agent tool strategy that matches the task, the risk, and the operating environment.
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