Agentic AI

An agentic AI system is one that acts, not just responds. Rather than producing a single output for a single input, an agent receives a goal, breaks it down into steps, uses available tools to execute those steps, and iterates based on intermediate results until the goal is achieved or it determines it cannot proceed.

What makes a system agentic

Three properties distinguish agents from standard LLM prompting:

• Planning: The model decomposes a goal into subtasks and sequences them.
• Tool use: The model calls external functions (search, code execution, APIs, file I/O) based on what it needs.
• Feedback loops: The model observes the results of its actions and adjusts behavior accordingly.

Agent patterns

Common architectures include:

• ReAct (Reason + Act): Interleave reasoning steps with tool calls. The model "thinks out loud" before choosing an action.
• Plan-and-execute: Generate a full plan first, then execute steps with individual calls.
• Multi-agent: Orchestrator assigns tasks to specialized sub-agents (researcher, coder, reviewer).

Real-world examples

Agentic AI powers systems like Claude Code (reads your repo, writes code, runs tests, fixes failures), Devin (autonomous software engineering), and browser automation agents (navigates the web to complete tasks). Many production agents include a human-in-the-loop approval step before irreversible actions. Enterprise adoption is growing, with platforms like Mendo helping organizations scale agentic AI workflows across infrastructure and operations.

Current limitations

Long-horizon reliability remains the primary challenge. Models accumulate errors over many steps, where a mistake at step 3 may only surface at step 15. Context windows, cost per step, and latency compound this problem. Most production agents work best on well-scoped, bounded tasks with clear success criteria.