AI Agents & Agent Development

What it is

An AI agent is a system in which a language model decides what to do next: it reads a goal, chooses a tool (search, database, API, code execution), observes the result, and repeats until the task is done or it needs a human. Where a chatbot answers, an agent acts — filing tickets, reconciling records, researching, writing and testing code.

Agent development is mostly software engineering: well-designed tools, clear permissions, durable state, observability and evaluation. The model supplies judgment; the surrounding system supplies safety and reliability.

How it works

1. Define the goal

   Give the agent a clear objective, constraints, and a definition of done.

2. Provide tools

   Expose narrowly scoped, well-described functions — ideally through a standard such as the Model Context Protocol.

3. Loop

   The model plans, calls a tool, reads the result and decides the next step, within step and budget limits.

4. Keep state

   Persist progress, memory and intermediate results so long tasks can resume and be audited.

5. Escalate

   Pause for human approval before irreversible or high-impact actions.

6. Evaluate

   Test full trajectories — did it reach the goal, with the right tools, safely and efficiently?

Key concepts

Tool / function calling

The mechanism by which a model requests an action with structured arguments.

Model Context Protocol (MCP)

An open standard for connecting AI applications to tools and data sources through reusable servers.

Workflows vs. agents

Fixed, code-defined steps are cheaper and more predictable; use autonomous agents only where flexibility pays.

Multi-agent systems

Specialized agents coordinated by an orchestrator, or communicating over protocols such as Agent2Agent (A2A).

Memory

Short-term working context plus long-term stores of facts, preferences and past outcomes.

Least privilege

Each tool gets only the permissions it needs; destructive actions require confirmation.

Durable execution

Checkpointing so long-running agents survive failures and restarts.

Tools & platforms

Skills employers look for

• Deciding when a deterministic workflow is better than an autonomous agent.
• Designing tool interfaces with clear names, descriptions, schemas and error messages.
• Building and consuming MCP servers to connect agents to enterprise systems.
• Implementing human approval steps, permission scoping and sandboxing.
• Managing state, memory, retries and long-running execution.
• Tracing and evaluating agent trajectories, cost and success rate.

Enterprise use cases

Common pitfalls

• Agents where a script would do — autonomy adds cost and unpredictability; use it deliberately.
• Over-broad tool permissions — an agent can only do as much damage as its tools allow.
• No step or budget limits — loops can burn tokens and time without making progress.
• Evaluating only final answers — inspect the path taken, not just the result.