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How to Build an AI Agent for Your Business

April 20, 2026•10 min read•Michael Ridland

You want to build an AI agent for your business. Not a chatbot that answers FAQs - a system that actually does things. Looks up orders, processes requests, coordinates across systems, and handles tasks your team currently does manually.

Good. AI agents are delivering real value for Australian businesses right now. But the path from "we want an AI agent" to "we have an AI agent running in production" has a lot of steps, and some of them aren't obvious.

Here's how to do it properly, based on the dozens of agent projects we've delivered at Team 400.

Step 1 - Pick the Right Use Case

This is where most failed AI agent projects go wrong. They pick the wrong problem to solve.

A good first AI agent use case has these characteristics:

High volume. The task happens at least 100 times per month. Less than that and the economics don't justify the build cost.

Pattern-based. The task follows recognisable patterns even if there's variation. "Process this insurance claim" follows patterns. "Write a creative marketing strategy" doesn't.

Defined inputs and outputs. You can clearly describe what information comes in and what the expected result looks like.

Moderate consequences for errors. If the agent gets it wrong, it's fixable. Don't start with financial transactions or medical decisions.

Currently done by humans who find it tedious. If your team dreads this task, the agent will probably handle it well because tedious-for-humans usually means pattern-based-for-machines.

Some use cases we've seen work well as first agents:

  • Customer service triage and Tier 1 resolution
  • Document processing and data extraction
  • Internal knowledge search and summarisation
  • Appointment scheduling with multiple constraints
  • Order status enquiries and basic modifications
  • Quote generation from defined parameters

Some use cases that look appealing but are harder than they seem:

  • Sales outreach (personalisation is genuinely hard)
  • Complex approval workflows (too many edge cases initially)
  • Anything requiring access to 10+ systems (start smaller)

Step 2 - Map the Process in Detail

Before writing any code, document exactly how a human does this task today. Every step, every decision, every exception.

We use a process mapping exercise that looks like this:

Main flow: The happy path. What happens 70-80% of the time.

Decision points: Where does the human make a judgment call? What information do they use? What are the possible outcomes?

Exceptions: What happens in unusual cases? How are they handled today? Which exceptions happen often enough to be worth automating?

Escalation criteria: When does a human need to step in? What triggers escalation?

Systems touched: What applications and data sources does the human access during this process?

Time and effort: How long does each step take? Where are the bottlenecks?

This exercise consistently reveals things the project sponsor didn't know. Hidden steps. Undocumented exceptions. Workarounds that have become institutionalised. Better to discover these now than during development.

We typically spend 2-3 days on process mapping for a moderately complex agent. It's the best time investment in the entire project.

Step 3 - Choose Your Architecture

There are several ways to build an AI agent. The right choice depends on your use case complexity and existing technology stack.

The LLM-Orchestrated Agent

This is the most common pattern for business agents today. A large language model (like GPT-4, Claude, or an Azure OpenAI deployment) acts as the reasoning engine, with tools connected for external actions.

User Input
    |
    v
LLM (Reasoning Engine)
    |
    +-- Tool: Search Knowledge Base
    +-- Tool: Look Up Customer in CRM
    +-- Tool: Check Order Status in ERP
    +-- Tool: Create Support Ticket
    +-- Tool: Send Email
    |
    v
Response to User

The LLM decides which tools to call, in what order, and how to combine the results into a coherent response.

Best for: Customer service agents, internal assistants, process automation where the AI needs to reason about what to do next.

Strengths: Flexible, handles unexpected inputs well, can explain its reasoning.

Weaknesses: Costs more per interaction (LLM API calls add up), latency from multiple LLM calls, harder to guarantee deterministic behaviour.

The Workflow Agent

For processes that follow a defined sequence with some AI-powered steps, a workflow engine with AI at specific decision points can be more appropriate.

Trigger (new email, form submission, schedule)
    |
    v
Step 1: Extract data (AI)
    |
    v
Step 2: Validate against rules (code)
    |
    v
Step 3: Classify and route (AI)
    |
    v
Step 4: Generate response (AI)
    |
    v
Step 5: Send and log (code)

Best for: Document processing, claims handling, structured intake processes.

Strengths: Predictable, easier to test, lower cost per interaction, easier to debug.

Weaknesses: Less flexible with unexpected inputs, requires more upfront design, harder to handle conversational interactions.

The Hybrid

Most production agents we build end up being hybrid - a workflow structure for the overall process with LLM-based reasoning at specific steps where flexibility is needed.

This gives you the cost efficiency and predictability of workflows with the intelligence of LLMs where it matters.

Step 4 - Select Your Technology Stack

Here's what we typically recommend for Australian businesses:

The AI Layer

Azure OpenAI Service: Our default recommendation for enterprise deployments. Data stays in-region (Australia East), you get SLAs, and it integrates well with the broader Azure ecosystem. GPT-4o for complex reasoning, GPT-4o-mini for simpler tasks to manage costs.

Azure AI Foundry: For agents that need multiple AI capabilities - language understanding, document processing, search. AI Foundry ties these together in one platform.

Direct API (OpenAI, Anthropic): Fine for prototypes and non-regulated industries. Less control over data residency. We use these during development and proof-of-concept phases.

The Orchestration Layer

Semantic Kernel (.NET): Our preferred framework for production agents in .NET environments. Mature, well-supported by Microsoft, good tooling.

LangChain / LangGraph (Python): Popular in the Python ecosystem. Good for prototyping. More effort to make production-ready.

Custom orchestration: For simpler agents, sometimes a clean custom implementation is better than a framework. Less magic, easier to debug.

The Infrastructure

Azure for hosting: App Service or Container Apps for the agent runtime. Azure AI Search for knowledge retrieval. Azure Service Bus for async operations.

Monitoring: Application Insights for performance. Custom dashboards for agent-specific metrics (resolution rate, escalation rate, tool call patterns).

Step 5 - Build the Core Agent

Now you're writing code. Here's the development sequence that works best.

Week 1-2: Build the tool layer

Start with the connections to external systems. Build and test each tool independently. This is the foundation everything else depends on.

For each tool:

  • Define the input schema
  • Implement the API call
  • Handle errors and edge cases
  • Write integration tests
  • Add logging

Week 3-4: Build the reasoning layer

Connect the LLM to your tools with appropriate prompts. The system prompt is where you define the agent's personality, capabilities, and constraints.

A production system prompt typically includes:

  • Who the agent is and what it does
  • What tools are available and when to use them
  • Rules and constraints (what the agent must never do)
  • Escalation criteria
  • Response format guidelines
  • Examples of good and bad interactions

Week 5-6: Handle the edges

This is where you deal with:

  • Conversations that go off-track
  • Users who try to trick or abuse the agent
  • Multi-turn interactions where context builds over time
  • Concurrent operations and race conditions
  • Timeouts and partial failures

Week 7-8: Test and refine

Test with real scenarios. Not just the happy path - the messy, ambiguous, frustrating scenarios that real users create.

Run the agent through at least 100 real or realistic conversations. Track:

  • Task completion rate
  • Accuracy of information provided
  • Appropriate escalation decisions
  • Response time
  • User satisfaction (if you can measure it)

These timelines are for a moderately complex agent. Simple agents can be faster. Complex ones take longer.

Step 6 - Deploy Safely

Don't go from zero to full production overnight.

Phased Rollout

Phase 1 - Shadow mode (2 weeks): The agent processes real requests but doesn't respond directly to users. Humans see the agent's proposed responses alongside their own. This lets you evaluate quality without risk.

Phase 2 - Limited deployment (2-4 weeks): The agent handles a subset of interactions, maybe 10-20% of volume, or only certain types of requests. Humans handle the rest.

Phase 3 - Expanded deployment: Gradually increase the agent's scope based on performance data. Add new capabilities one at a time.

Phase 4 - Full deployment: The agent handles the full scope of its designed use case. Humans are on standby for escalations.

Monitoring in Production

Once live, monitor:

  • Resolution rate: What percentage of interactions does the agent complete successfully?
  • Escalation rate: How often does it hand off to a human? Is this appropriate?
  • Error rate: How often does it make mistakes? Are they trending up or down?
  • Response time: Is it fast enough for users?
  • Cost per interaction: Are API costs within budget?
  • User feedback: Are users satisfied? Are they using it again?

Set up alerts for anomalies. If the error rate spikes, you want to know immediately, not when a customer complains.

What It Costs

Real numbers from our projects:

Simple agent (single task, 2-3 tool integrations, text-only):

  • Build: $40,000-$70,000
  • Monthly run cost: $500-$2,000
  • Timeline: 6-8 weeks

Moderate agent (multiple tasks, 4-6 integrations, conversational):

  • Build: $80,000-$150,000
  • Monthly run cost: $2,000-$6,000
  • Timeline: 10-14 weeks

Complex agent (enterprise integration, compliance requirements, multiple channels):

  • Build: $150,000-$350,000
  • Monthly run cost: $5,000-$15,000
  • Timeline: 16-24 weeks

Running costs scale with usage. The LLM API calls are the largest component, followed by infrastructure, then monitoring and maintenance.

Common Mistakes and How to Avoid Them

Skipping the process mapping. You can't automate a process you don't fully understand. Every agent project we've seen fail skipped or rushed this step.

Over-engineering the first version. Build the 80% case first. Handle the remaining 20% through escalation initially, then add coverage in iterations.

Not planning for maintenance. AI agents aren't set-and-forget. Models change, APIs update, user needs evolve. Budget 15-20% of the build cost annually for ongoing maintenance.

Ignoring the human handoff. The transition from AI to human needs to be smooth. The human should see full context of the AI conversation, not start from scratch.

Testing only with clean data. Real users type poorly, provide partial information, change their minds mid-conversation, and ask things that are technically out of scope. Test with messy, realistic scenarios.

Build vs Buy

You have options:

Off-the-shelf AI agents (Intercom Fin, Zendesk AI, etc.): Good if your use case matches their template. Limited customisation. Fastest to deploy. Cheapest initially but can get expensive at scale.

Low-code platforms (Voiceflow, Botpress): More flexibility. Good for simpler agents. Require some technical capability but not a full development team.

Custom build: Full control. Best for differentiated use cases or deep system integration. More expensive, but you own everything and can optimise exactly how you need.

Our general recommendation: prototype on a low-code platform to validate the use case, then decide whether to scale there or go custom based on what you learn.

Getting Started with Team 400

We're AI agent developers based in Australia who've built agents across customer service, document processing, field service, and internal operations. Our approach starts with understanding your process, then building the right agent for your specific situation.

We work primarily with Azure AI Foundry for enterprise deployments and can connect to virtually any business system through our integration services.

If you're thinking about building an AI agent, get in touch. We'll tell you honestly whether an agent is the right approach for your use case and what it will realistically take to build.