Understanding how agents observe, think, and act — and how they're changing the way software gets built.
You've learned about LLMs that respond to prompts. An AI Agent is the next step — an LLM given a goal and the ability to take actions to achieve it. Instead of just talking, agents are designed to do.
Can give you a recipe for chocolate chip cookies.
Accesses your calendar, checks your grocery list, orders missing ingredients online, and schedules time for you to bake.
From providing information → to performing tasks.
Agents work in a continuous cycle — repeating until the goal is complete.
Goal: "Plan a weekend trip to San Francisco next month, including flights and a hotel."
The agent doesn't respond immediately — it thinks through a structured plan first:
A model can't book flights on its own. It needs help. Agents perform actions using tools — programs or functions the agent can call to interact with the outside world or perform specific tasks.
The agent doesn't just select a tool — it provides all required details as parameters, then uses the result to inform the next step.
ACTION Call flight_search_api: { "destination": "San Francisco", "departure_month": "November", "trip_type": "weekend" } RESULT { "flights": [{ "flight": "UA249", "price": 345, "depart": "2025-11-14T08:00" }] }
Results return as JSON — a lightweight format both humans and machines can read.
The trajectory is the full path of actions and observations the agent has taken. It's the agent's memory — and why maintaining context across steps is critical.
Tools sometimes fail. Instead of giving up, agents are designed to enter a recovery loop and find a new path to the goal.
AI agents are already inside tools you might use every day — the loop and tools you just learned are what power them.
The common thread: a clear goal, the Observe → Think → Act loop, and the right tools to act on the world.
Agentic coding is a development approach where LLM-driven agents autonomously plan tasks, generate code, run it, and iterate until a goal is met — fundamentally different from single-shot code generation.
Agentic coders use a specialized set of tools to read, write, run, and verify code — autonomously and iteratively.
Goal: "Fix the failing test."
The agent inspects, proposes, tests, and retries — iterating until the goal is achieved without human intervention at each step.
Agentic coding delivers measurable gains in productivity and quality by enabling autonomous, multi-step development work.
Autonomy introduces new operational, security, and governance risks that must be managed with testing, oversight, and guardrails.