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The AI Agent Development Process: From Concept to Production

May 14, 2025•6 min read•Team 400

Everyone wants to talk about AI agents. Few want to talk about how to actually build them well.

Here's the process we've developed over dozens of AI agent projects. It's not glamorous. It's methodical, iterative, and sometimes frustrating. But it works.

Phase 0: Should You Build This?

Before writing any code, answer these questions:

Is an agent the right solution?

  • Could a simple rule-based system work? (Often yes, and it's cheaper)
  • Could a search/RAG system work without autonomy?
  • Does the task actually require reasoning and decision-making?

Do you have the prerequisites?

  • Clear process documentation
  • Representative data for testing
  • Access to systems the agent needs
  • Stakeholder alignment on scope

Are you prepared for ongoing investment?

  • Agents aren't set-and-forget
  • Budget for monitoring, maintenance, improvement
  • Plan for edge cases and failures

Many "AI agent" projects should actually be workflow automation or chatbot projects. That's not a failure—it's appropriate scoping.

Phase 1: Process Understanding

You can't automate what you don't understand. This phase is about deep understanding of the task.

Activities

Shadow current workers: Watch people do the job. Not how they describe it—how they actually do it. Note decision points, exceptions, informal knowledge.

Document the happy path: The standard flow from input to output. Every step, every decision, every system touched.

Catalogue exceptions: What breaks the standard flow? How often? How do humans handle it? Document at least 20 real exceptions.

Map decision logic: For each decision point, what information is considered? What are the possible outcomes? What confidence level triggers escalation?

Identify boundaries: What should the agent definitely not do? Where does human judgment remain essential?

Deliverables

  • Process flowchart with decision points
  • Exception catalogue with frequency estimates
  • Decision logic documentation
  • Clear scope boundaries
  • Initial metrics baseline

Time: 2-4 weeks

This phase feels slow. Teams want to start building. But every hour here saves ten hours later.

Phase 2: Architecture Design

Now that you understand the problem, design the solution.

Key Decisions

Agent type:

  • Single-purpose agent (one task done well)
  • Multi-tool agent (orchestrates across capabilities)
  • Multi-agent system (specialised agents coordinating)

Start simpler than you think you need.

Human-in-the-loop design:

  • What's autonomous?
  • What needs approval?
  • What's human-only?

Default to more human involvement, relax as confidence grows.

Integration approach:

  • Which systems need read access?
  • Which need write access?
  • What APIs exist vs need building?

State management:

  • What does the agent need to remember?
  • Across a conversation? Across sessions?
  • How is state persisted?

Observability:

  • What gets logged?
  • What metrics matter?
  • How do you debug failures?

Deliverables

  • Architecture diagram
  • Integration specifications
  • Data model
  • Security design
  • Monitoring plan

Time: 1-2 weeks

Phase 3: Prompt Engineering

This is where the "AI" happens. But it's less magic and more engineering.

System Prompt Development

The system prompt defines who the agent is and how it behaves. Key elements:

Role definition: Who is the agent? What's its purpose?

Capabilities: What can it do? What tools does it have?

Constraints: What should it never do? What requires escalation?

Tone and style: How should it communicate?

Error handling: What should it do when uncertain?

Tool Definitions

Each tool the agent can use needs:

  • Clear description of purpose
  • Input parameters with types and constraints
  • Output format
  • Error conditions
  • Examples of appropriate use

Few-Shot Examples

Provide examples of good behaviour:

  • Example conversations showing ideal flow
  • Examples of appropriate tool use
  • Examples of correct escalation
  • Examples of handling edge cases

Iterative Refinement

Prompt engineering is empirical. You:

  1. Write initial prompts
  2. Test against scenarios
  3. Identify failures
  4. Refine prompts
  5. Repeat

Plan for 3-5 major iterations minimum.

Time: 2-4 weeks

This phase takes longer than most teams expect.

Phase 4: Integration Development

Connecting the agent to real systems.

Tool Gateway

Build a single gateway for all external interactions:

  • Authentication handling
  • Rate limiting
  • Logging
  • Error handling
  • Input validation

Don't let the agent call external APIs directly.

Data Retrieval

If the agent needs knowledge:

  • Document indexing and embedding
  • Vector database setup
  • Retrieval pipeline
  • Chunking and ranking strategy

Test retrieval quality independently before connecting to agent.

System Integrations

For each system the agent touches:

  • Authentication setup
  • API client development
  • Error handling
  • Retry logic
  • Timeout configuration

Time: 3-6 weeks

Highly variable based on integration complexity.

Phase 5: Testing

AI testing is different from traditional software testing.

Functional Testing

  • Does each tool work correctly?
  • Does retrieval return relevant results?
  • Do integrations handle errors gracefully?

Conversation Testing

  • Build a test suite of scenarios
  • Cover happy paths, edge cases, and adversarial inputs
  • Automate evaluation where possible
  • Include human evaluation for quality

Load Testing

  • Can the system handle expected volume?
  • How does performance degrade under load?
  • What's the cost per interaction at scale?

Security Testing

  • Can the agent be manipulated to bypass controls?
  • Are credentials protected?
  • Is data properly isolated?

User Acceptance Testing

  • Real users, real scenarios
  • Gather qualitative feedback
  • Identify confusion points

Time: 2-4 weeks

Don't rush this. You'll find issues here or in production. Here is cheaper.

Phase 6: Deployment

Launching into the real world.

Staged Rollout

  1. Internal pilot (your own team)
  2. Friendly customer pilot (willing partners)
  3. Limited GA (subset of users/scenarios)
  4. Full GA

Each stage should have clear success criteria for progression.

Monitoring Setup

Before launch:

  • Dashboards for key metrics
  • Alerting for anomalies
  • Log access for debugging
  • Escalation procedures

Fallback Planning

  • What happens if the agent fails?
  • How do users reach a human?
  • What's the rollback plan?

Time: 2-4 weeks

Phase 7: Stabilisation

The first month in production.

Activities

  • Review conversations daily
  • Identify failure patterns
  • Quick fixes for critical issues
  • Gather user feedback
  • Tune thresholds

Expect

  • Things you didn't anticipate
  • Edge cases that weren't in your test suite
  • Users doing things you didn't expect
  • Performance variations

Time: 4-8 weeks

Plan for intense attention during this period.

Ongoing: Operations

Now it's a running system.

Regular Activities

  • Weekly conversation review
  • Monthly metrics review
  • Quarterly prompt/model updates
  • Periodic retraining of any ML components

Continuous Improvement

  • Track common issues
  • Prioritise improvements
  • A/B test changes
  • Measure impact

This isn't a phase—it's permanent. Budget accordingly.

Timeline Summary

Phase Duration
0. Scoping 1-2 weeks
1. Process Understanding 2-4 weeks
2. Architecture 1-2 weeks
3. Prompt Engineering 2-4 weeks
4. Integration 3-6 weeks
5. Testing 2-4 weeks
6. Deployment 2-4 weeks
7. Stabilisation 4-8 weeks

Total: 4-8 months for a production AI agent.

If someone promises faster, ask what they're cutting.

Our Approach

This process is what we follow for AI agent projects. It's been refined through both successes and failures.

We've built agents for customer service, field operations, and document processing. Each project taught us something.

If you're building an AI agent, we're happy to share more detail on any phase.

Talk to us