Introduction: coding agent and why it matters
A coding agent is a looped system that uses LLM tokens to automate development tasks; building one is a core 2025 skill for practical AI productivity.
Context
Geoffrey Huntley breaks down agents to their fundamentals: typically a few hundred lines of code running a token loop. Agentic models (e.g., Sonnet, Kimi K2) prioritize action, while other models can be wired as tools to add reasoning and checks.
Article summary
Huntley argues there is no magic moat: agents are loops of LLM tokens. Pick an agentic model for momentum and wire an "Oracle" (GPT as a tool) for validation and research. This pattern underpins products like Amp and other developer tools. Source: Geoffrey Huntley — https://ghuntley.com/agent/
The challenge
Users often compare models by context window or cost only, missing specialization and orchestration required for robust agents.
Solution / Approach
Practical steps: 1) choose an agentic LLM for action; 2) attach specialist models as tool-oracles for checks; 3) implement a token loop with incremental success criteria.
Conclusion
Knowing how to build a coding agent moves you from AI consumer to AI producer, enabling concurrent work and automation of routine engineering tasks.
FAQ
What is a coding agent?
A coding agent is software that repeatedly interacts with an LLM token loop to plan, act and refine code or development tasks.
Why learn to build a coding agent in 2025?
Because it enables automation during meetings and workflows, boosting productivity and becoming a sought-after capability.
Which models are recommended for agentic behavior?
Huntley highlights agentic models like Claude Sonnet or Kimi K2, and suggests wiring GPT as an Oracle for higher-level reasoning.
What limitations should teams watch for?
Watch for token costs, model hallucinations and the need for verification layers; human oversight remains essential.