I define an AI agent as an LLM that perceives its environment, plans, uses tools, and learns, and I capture its operation in a repeatable five‑step loop: Get the Mission, Scan the Scene, Think It Through, Take Action, Learn & Get Better.
- Level 0 – Core LLM reasoning only, no tool use, memory, or external data access.
By the end of 2024, AI‑agent startups had raised more than $2 billion, and the market was valued at $5.2 billion.
It's expected to explode to nearly $200 billion in value by 2034.
I adopt the five‑step loop as the canonical workflow because it captures goal definition, context acquisition, planning, execution, and continuous improvement in a repeatable cycle, and I use the capability levels as a roadmap for incremental system design.
- Level 1 – Connected to external tools (search, APIs, databases) enabling multi‑step actions and current‑event awareness.
- Level 2 – Strategic Problem‑Solver that sequences multiple tools, employs context engineering to curate short, focused inputs for each step, and operates autonomously after an initial trigger.