What Is an AI Agent? A Complete Guide for Business Leaders

Everyone's talking about AI agents. Half of them don't know what they mean.

Let's fix that. After building AI agents for Australian businesses over the past two years, here's the plain-English explanation your vendor won't give you.

The Simple Definition

An AI agent is software that can:

1. Understand a goal
2. Figure out the steps to achieve it
3. Take those steps autonomously
4. Adjust when things don't go as planned

That's it. No magic, no sentience, no robot uprising.

The difference between an AI agent and a regular chatbot? A chatbot answers questions. An agent completes tasks.

Ask a chatbot "What's the status of order #1234?" and it tells you. Ask an agent "Make sure order #1234 ships today" and it checks inventory, contacts the warehouse, updates the customer, and escalates to a human if something's blocked.

Why Now?

AI agents aren't new conceptually. What's new is that they actually work reliably enough for business use.

Three things changed:

Language models got good: GPT-4, Claude, and their competitors can now understand context, follow multi-step instructions, and recover from errors in ways that weren't possible three years ago.

Tool integration matured: Agents can now reliably call APIs, query databases, send emails, and interact with other software. The plumbing exists.

Cost dropped: Running an agent interaction that would have cost $2 in 2023 costs $0.15 today. That changes the economics dramatically.

What Agents Can Actually Do Today

Here's where we need to separate reality from vendor demos.

Agents are good at:

• Processing documents and extracting information across multiple formats
• Customer service for defined problem sets (order status, booking changes, FAQ)
• Data gathering and summarisation across sources
• Scheduling and coordination with multiple constraints
• First-pass analysis and triage of incoming requests

We built a conversational AI assistant that handles 73% of incoming customer queries without human involvement. That's real, it's been running for 18 months, and the client's NPS actually went up.

Agents are not good at (yet):

• Anything requiring genuine creativity or novel problem-solving
• High-stakes decisions with legal or safety implications
• Tasks requiring real-world physical interaction
• Situations where being wrong has severe consequences

The honest answer is that agents are really good at tasks that are boring for humans but require some judgment. They're terrible at tasks that are interesting for humans or genuinely ambiguous.

The Architecture (Non-Technical Version)

An AI agent typically has four components:

1. The Brain: Usually a large language model that does the reasoning
2. Memory: Short-term (current conversation) and long-term (past interactions, learned preferences)
3. Tools: Connections to other systems—your CRM, calendar, database, email
4. Guardrails: Rules about what the agent can and can't do

The sophistication of an agent comes from how well these components work together, not from any single one being more advanced.

Do You Need One?

Here's a quick test. You might benefit from an AI agent if:

• You have staff doing repetitive tasks that require some judgment but follow patterns
• Your customers frequently ask questions that have answers, just scattered across systems
• You have processes that involve gathering information from multiple sources
• You're losing business because humans can't respond fast enough (after hours, peak times)
• Your team is doing "human glue" work—copying data between systems, sending routine updates

You probably don't need an AI agent if:

• Your processes are genuinely unique each time
• You have fewer than 100 similar tasks per month (the economics don't work)
• Your data is a mess and you haven't fixed it
• You're not prepared to monitor and improve the agent over time

What It Costs

Real numbers from our projects:

Simple agent (single task, limited tools): $30,000–$60,000 to build, $500–$2,000/month to run

Moderate agent (multiple tasks, CRM/email integration): $60,000–$120,000 to build, $2,000–$5,000/month to run

Complex agent (enterprise integration, custom workflows, compliance requirements): $150,000–$400,000 to build, $5,000–$15,000/month to run

The running costs are primarily API calls to the language model plus infrastructure. Higher volume = lower unit cost but higher total cost.

The Build Decision

You've got three options:

Off-the-shelf platforms (Intercom Fin, Zendesk AI, etc.): Cheapest to start, limited customisation, you're locked into their ecosystem. Good for standard customer service if you're already on their platform.

Low-code tools (Voiceflow, Botpress, etc.): Middle ground, more flexibility, still constrained by what the platform supports. Good for simpler use cases with technical-ish internal team.

Custom build: Most expensive, most flexible, you own everything. Good for differentiated use cases or when you need deep integration with existing systems.

We typically recommend starting with a proof of concept on a low-code tool, then deciding whether to scale there or go custom based on what you learn.

Common Mistakes

Building before understanding: We've rescued three projects where someone built an agent, then realised the underlying process was broken. Fix the process first.

Over-automating: Not every human touchpoint should be replaced. Sometimes people want to talk to people, especially when they're upset or confused.

Under-monitoring: Agents aren't set-and-forget. They need ongoing tuning, and you need visibility into what they're doing. Budget for this.

Ignoring edge cases: The demo works perfectly. Then a customer types in all caps with typos while angry, and everything breaks. Test with real, messy data.

Getting Started

If you're exploring AI agent development for your business, here's the practical path:

1. Identify one process that's high-volume, pattern-based, and low-risk if mistakes happen
2. Document it thoroughly—every exception, every edge case
3. Run a proof of concept with real (anonymised if needed) data
4. Measure against clear metrics: resolution rate, accuracy, time saved, cost
5. Decide to scale or pivot based on evidence, not excitement

We're happy to have a no-pressure conversation about whether an agent makes sense for your situation. Sometimes the answer is "not yet" or "not ever," and that's fine.

Talk to our team about your specific use case.