How We Build AI Agents: From Requirements to Production

Phase 1: Discovery and Scoping

We start by understanding what you actually need, not what you think you need. This involves:

• Process mapping — We document your current workflows, identifying where human judgment is required and where decisions could be automated
• Data inventory — We catalog the data sources agents will access, their formats, quality, and access patterns
• Integration requirements — We identify the systems agents must connect to and the available interfaces
• Success criteria — We define measurable outcomes: processing time, accuracy rates, exception rates

The output is a scope document that describes what the agent will do, what it will not do, and how we will know if it is working.

Phase 2: Architecture Design

Before writing code, we design the system architecture:

• Agent decomposition — Complex tasks often require multiple specialized agents rather than one monolithic agent
• Data flow design — How information moves through the system, where it is stored, how long it is retained
• Error handling strategy — What happens when things go wrong: retry logic, fallbacks, escalation paths
• Security model — Access controls, credential management, audit requirements

Architecture decisions are documented and reviewed with your technical team. We prefer boring, proven patterns over novel approaches—reliability matters more than elegance.

Phase 3: Prompt Engineering

Prompts are the core of agent behavior. We treat them as first-class artifacts:

• Iterative development — Prompts are refined through systematic testing against real examples from your domain
• Version control — Every prompt revision is tracked, with clear documentation of what changed and why
• Edge case handling — We specifically test prompts against unusual inputs, ambiguous cases, and potential failure modes
• Evaluation metrics — We measure prompt performance quantitatively: accuracy on test sets, consistency across similar inputs, appropriate handling of out-of-scope requests

The goal is prompts that produce predictable, correct behavior across the range of inputs the agent will encounter in production.

Phase 4: Development

We build agents using standard software engineering practices:

• Typed code — TypeScript or Python with type hints, catching errors at development time
• Modular design — Separate concerns: LLM calls, data access, business logic, API interfaces
• Dependency injection — Components are loosely coupled, enabling testing and modification
• Configuration management — Environment-specific settings separated from code

Code is written for maintainability. Future developers (including your team) should be able to understand and modify the system without archaeological research.

Phase 5: Testing

AI agents require multi-layered testing:

• Unit tests — Individual functions and components tested in isolation
• Integration tests — Components tested together, including real LLM calls in test environments
• Evaluation sets — Curated examples that verify agent behavior on representative inputs
• Regression tests — Specific cases from bugs or edge cases, preventing recurrence
• Load tests — Performance verification under expected production load

We maintain test suites that run automatically on every change. When behavior drifts (due to model updates, for example), tests catch it early.

Phase 6: Deployment

Deployment is not an event but a process:

• Staging environment — Agents run in a production-like environment with synthetic or anonymized data
• Gradual rollout — New agents process a subset of traffic initially, expanding as confidence grows
• Monitoring setup — Metrics, logs, and alerts configured before going live
• Runbook documentation — Procedures for common operational scenarios

For self-hosted deployments, we provide deployment artifacts matching your infrastructure: Docker images, Kubernetes manifests, Terraform modules.

Phase 7: Ongoing Operation

After deployment, agents require ongoing attention:

• Performance monitoring — Tracking accuracy, latency, error rates over time
• Model updates — Evaluating new model versions, updating prompts as needed
• Feedback loops — Incorporating corrections and improvements based on production experience
• Capacity planning — Adjusting resources as usage patterns evolve

We offer ongoing maintenance agreements or can transfer operational responsibility to your team with full documentation and training.

What Makes This Different

Many AI projects fail because they skip steps or treat AI as magic. Our approach is deliberately unglamorous:

• We do discovery before proposing solutions
• We design before coding
• We test systematically, not just optimistically
• We plan for operation, not just launch

This takes more time upfront but produces systems that work reliably and can be maintained long-term.

Related Topics

Learn why teams choose custom development over frameworks in our LangChain alternative comparison. For specific use cases, explore backend automation applications.