Definition
LLM using tools in a loop with context
This definition of an AI agent clicked for me. Not the marketing fluff about "autonomous systems" or "intelligent assistants." An agent is simply an LLM that can use tools, runs in a loop until the task is done, and maintains context throughout.
The Difference
Agent vs Autocomplete
AGENT
LLM + Tools + Loop + Context
COPILOT
Autocomplete ≠ Agent
Evolution
The Rise of Agentic Coding
Anthropic released Claude Code, the first true terminal agent. It could autonomously navigate codebases, run tests, and ship changes.
Cursor released "agents in your terminal" 11 months after Claude Code. The rest of the industry scrambled to catch up.
Where are you on the path from autocomplete to agentic? Teams who master this transition ship quality software 3-5x faster.
Advantage
Why Terminal Agents Win
IDE agents are sandboxed. Terminal agents have full system access.
Hooks for pre/post actions. Custom MCP servers. Integration with any CLI tool.
Write and execute bash scripts. Configure AWS/GCP/Azure via CLI. Infrastructure as code.
Git hooks and pre-commit. CI/CD automation. Build and deploy workflows.
File system operations. Process management. Network and API calls.
Breakthrough
The Learning Loop
The loop naturally learns through iteration. Each cycle's "think" is informed by the previous cycle's "observe" — that is reflection. The iteration itself creates learning.
"After writing my first LLM loop, I lost sleep the next night. I lay awake having visions of the agentic future, seeing how every repetitive task, every debugging session, every code review could be transformed.
Once you build your first loop, your perspective changes. You stop thinking about AI as autocomplete and start seeing it as a collaborator that gets better with each iteration. That's the moment everything clicks."
Kaido Koort
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