How they work, why they matter, and what you should know before building one
AI agents are quickly becoming one of the most talked-about developments in applied AI. But despite the buzz, many people are still unsure what an “agent” actually is or how it differs from a standard large language model (LLM) prompt.
This post offers an introduction to AI agents: what they are, how they work, what makes them powerful, and what you should consider if you want to build or use them in the real world.
What Exactly is an AI Agent?
In a typical LLM interaction, you give a prompt, the model generates text, and the interaction ends. An agent is different. It’s a system built around an LLM that can:
- Make a plan of steps The LLM decides what needs to be done, often producing an internal chain-of-thought or high-level plan.
- Take actions and use tools Agents can call APIs, run code, fetch data, interact with databases, or trigger other services.
- Reflect before responding They can critique their own output, re-plan, or iterate on a task until they’re satisfied.
- Operate probabilistically Instead of a single deterministic output, agents are designed to act, experiment, and adjust.
In short: an agent is an LLM placed inside a structured workflow, with responsibilities, tools, and feedback loops.
A Real-World Example: Streamlining Underwriting
One example involves automating parts of an insurance underwriting workflow—a process that traditionally required humans to read broker emails, gather details, request missing information, and prepare quotes. This often took days or even weeks.
An agent-based version of this workflow can:
- Ingest incoming emails
- Interpret and extract relevant details
- Enrich the message with database information
- Request missing information automatically
- Pass everything to a separate quoting subsystem
To make the system understandable to non-technical users, a “command center” interface visualised tasks, email categorisation, and extracted information. It also allowed teams to monitor performance, apply guardrails, and evaluate outputs.
This example illustrates why agents matter: they can coordinate multi-step, real-world processes—something far beyond a single LLM prompt.
Key Lessons Learned When Implementing AI Agents
Building agents isn’t just about gluing an LLM to an API. Successful implementations require careful design across several areas.
1. Prompt Engineering and Structure
Small prompt changes can dramatically shift performance. Effective agent prompts are structured, deliberate, and version-controlled. Good prompts usually define:
- Role or persona
- Objective or goal
- Available context and constraints
- Guardrails and safety rules
2. Guardrails and Evaluation
Guardrails should exist both inside prompts and outside them in the agent’s system architecture. Meanwhile, evaluating an agent requires:
- Running tests at scale (hundreds or thousands of trials)
- Using scientific, programmatic performance measurement
- Comparing changes rigorously rather than relying on intuition
With multiple steps, actions, and decision points, a systematic evaluation approach is essential.
3. Operational Challenges
Some challenges are less technical and more organisational:
- Security for public-facing systems – Agents often need access to sensitive data or tools, which demands strong protections.
- Integration with old infrastructure – Many organisations rely on legacy systems that are not agent-friendly.
- Expectation management – Media hype creates misconceptions about what agents can reliably do.
- Lack of baseline data – Without understanding how humans currently perform a task, it’s hard to measure improvement.
These operational issues often become the biggest blockers.
Best Practices: Thinking of the Agent as a Team Member
A helpful mental model is to treat an agent like a new employee.
1. Define the Job
Specify the agent’s role, its scope, and what “good performance” looks like. Understand the current human workflow and decide where the agent fits in.
2. Provide Context
Agents operate far better when they have the equivalent of a “team handbook”: databases, guidelines, rules, examples, and documentation.
3. Integrate Fully
Give the agent the access it needs to be useful—data access, APIs, output destinations—and build an interface that helps people trust and monitor it.
4. Measure Performance
Break performance into:
- Guardrails – Protect inputs and outputs
- Scientific evals – Systematic testing
- User feedback – Thumbs up/down, comments, or formal reviews
This ongoing cycle is what improves an agent from prototype to production-ready.
Building Your Own Agent
There’s no single “correct” way to build an agent. You can start small and iterate or design a full system upfront.
Here are common approaches:
Custom GPT or Copilot-Style Systems
These allow you to store a persistent prompt, connect APIs, and add tools for interacting with other systems.
Workflow Builders
Platforms like n8n, Zapier, or Sim.AI use visual drag-and-drop blocks to build flows such as:
- Email arrives
- Content is passed to an LLM
- LLM chooses an action
- Output is emailed, stored in a database, or added to a workspace
One example flow used n8n to:
- Accept webhook inputs
- Classify the request
- Generate a report using GPT-style models and custom instructions
- Store the report in a workspace tool
- Extend the workflow to query client databases or send summary emails
These tools lower the barrier to entry, letting you prototype quickly while still enabling custom code when needed.
Agents Are the Future
AI agents are powerful because they can plan, act, reflect, and integrate deeply into real-world systems. But they’re not magic. Successful agent implementations require:
- Clear definitions of roles and responsibilities
- Careful prompt design
- Strong guardrails and scientific evaluation
- Thoughtful integration with existing workflows
- A mindset that treats agents like teammates, not toys
Start small, test rigorously, integrate responsibly, and iterate based on real user feedback. Do that—and agents can transform how work gets done.