The State of AI Agents in 2025

The 'chat' paradigm is shifting. In 2024, we asked LLMs to write code. In 2025, we act agents to 'deploy this feature'. The difference is agency: the ability to break down a high-level goal into a graph of dependent tasks, execute them against real tools (terminal, browser, IDE), and self-correct when errors occur.

I'm currently experimenting with multi-agent systems where a 'Planner' agent delegates to a 'Coder' and a 'Reviewer'. The friction is no longer intelligence; it's context window management and the reliability of the tools we give them.

Standardizing how agents interface with software is critical. The 'Model Context Protocol' (MCP) is emerging as a strong contender, allowing any developer to expose their API as a tool for an LLM without writing custom glue code. It's effectively 'Swagger for AI'.

Agents can get stuck in reasoning loops. 'Heuristics-based termination' is a fancy way of saying we need kill-switches. I implement strict budget caps (both financial and token-based) on every autonomous rum run.