Develop Your Agentic AI Prototype

IT leaders face rising pressure to demonstrate value from agentic AI, yet many organizations struggle to move agents into production and realize ROI due to insufficient evidence of solution feasibility, viability, and security. Follow our structured five-phase engineering methodology to turn agentic AI designs into defensible, evidence-generating prototypes. This framework focuses on technical implementation after the design process has been completed.

Organizations that successfully design agentic AI prototypes often stall at implementation, producing fragile demos that can’t survive real-world inputs, scale beyond a single developer’s laptop, or provide the evidence of solution feasibility, viability, and security that leadership needs to approve investment. To engineer agentic AI systems, traditional software development patterns must be supplemented with new patterns to address challenges such as build sequencing, orchestration complexity, limited observability into model reasoning, invisible costs, and the absence of standardized evaluation discipline.

1. Autonomy isn’t a substitute for architecture.

Production readiness doesn’t come from the model, it comes from the architecture underneath it. There has never been a better time to build, but waiting for a “perfect” model isn’t a strategy. Model capability sets the ceiling, but engineering determines how much of that ceiling you can reach.

2. You can’t optimize what you can’t see.

Optimization is a loop: run > observe > evaluate > refine. Evals and observability aren’t optional; they’re how you make progress. Every run should be instrumented to show what the agent saw, what it did, where it failed, how long it took, and what it cost.

3. Evaluation turns a demo into a decision.

A prototype becomes valuable when leadership can assess it. Clear evidence on performance, cost, and reliability is what turns a demo into an approved investment.

Use this step-by-step blueprint to move from fragile demo to a defensible system with measurable signal of business impact.

Our framework provides the methodology and supporting tools you need to build an agentic AI prototype in five key phases. (Note: Prior completion of our Design Your Agentic AI Prototype blueprint is required.)

Set up the development stack. Create a reproducible development environment and runnable repository.
Prepare data and build tools. At the end of this phase, you’ll have agent-ready data and custom tools for each integration point.
Build agents. This phase gives you a runnable agentic system with guardrails and human-in-the-loop elements.
Evaluate and optimize. The outcome of this phase is an observable, measured, and optimized prototype.
Document and showcase. This final phase produces a prototype demonstration with an evidence pack that supports scaling decisions.

Develop Your Agentic AI Prototype Research & Tools

1. Develop Your Agentic AI Prototype Deck – A comprehensive framework that guides you through five key phases to create a working agentic AI prototype.

This research is designed to help you:

Apply a disciplined build methodology that turns agentic AI designs into reproducible, defensible prototypes.
Embed guardrails, human-in-the-loop checkpoints, tracing, and cost controls directly into the build so safety and observability are designed in, not bolted on.
Deliver a working prototype with an evidence pack – eval results, cost projections, and a demo – that gives leadership the proof they need to fund scaling.

2. Develop Your Agentic AI Prototype Coding Tutorials – Step-by-step tutorials to walk you through the blueprint’s concepts in code.

These tutorials:

Are aligned to each phase of the blueprint.
Provide you with a tutorial for every concept in the framework’s methodology.
Teach you how to apply the concepts in Python.

3. Develop Your Agentic AI Prototype Development Template – A repository template that functions as a base to build your agentic AI prototype.

This template:

Provides your environment setup.
Includes agent scaffolding.
Can be copied to start building your own prototype.

4. Develop Your Agentic AI Prototype End-to-End Examples – A set of repositories featuring examples from a real Service Desk agentic AI system.

These examples include end-to-end Service Desk Triage agents featuring:

A decentralized orchestration pattern and orchestration by agent handoff.
A decentralized orchestration pattern and orchestration by code.
A top-level manager agent to coordinate sub-agents.

5. Develop Your Agentic AI Prototype Demo Presentation Template – A PowerPoint template to help build a compelling presentation that outlines the details leadership needs to greenlight production.

Use this template to build a leadership-ready presentation that:

Showcases the agent in validated scenarios alongside evaluation results, risk analysis, cost indicators, and ROI projections.
Outlines the solution to your problem statement, explains what was implemented, describes the evaluation process, reports the evaluation results, and spells out guardrails that were applied.
Defines key next steps.

Workshop: Develop Your Agentic AI Prototype

Workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn't enough, we offer low-cost delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.

Module 1: Set Up the Development Stack

The Purpose

Focus on creating a reproducible, runnable environment. If you can’t reliably build and run it, you won’t be able to evolve or scale it.

Key Benefits Achieved

A reproducible development environment and runnable repository.

Activities

Outputs

1.1

Determine agent development tech stack.

Chosen model access API, agent development library, and agent development platform.

1.2

Set up agent development tooling.

Git, IDE, and uv set up to build, test, and iterate quickly. Coding tutorials repository execution enabled to run code samples.

1.3

Initialize agent development template.

Cloned development template that includes scaffolding for agent development.

1.4

Responses API overview.

Understanding of model access, instructions, parameters, structured outputs, built-in tools, and streaming.

Module 2: Prepare Data & Build Tools

The Purpose

The real engineering is in the integrations. The model is a commodity. What differentiates your system is the tools, integrations, and scenarios you design.

Key Benefits Achieved

Agent-ready data and custom tools for each integration point.

Activities

Outputs

2.1

Prepare input data for test scenarios.

Clean, anonymize, and reformat source data as necessary so it can be easily understood by agents.

2.2

Initialize test scenarios.

Turn PRD test cases into runnable scenarios in scenarios.json – each with an id, description, payload, and any metadata needed to invoke the workflow.

2.3

Build tools for system integration.

Define the tools your agents will call to act on your systems – APIs, databases, file operations, and any MCP servers – and ensure they are wired up and ready to attach to agents in Phase 3.

Module 3: Build Agents

The Purpose

Without rigorous engineering, agents improvise. No orchestration, no guardrails, and no memory management leads to unpredictable behavior. The agent will “make it work,” but in an inconsistent and ungoverned way.

Key Benefits Achieved

A runnable agentic system with guardrails and human-in-the-loop.

Activities

Outputs

3.1

Create agents.

Create your individual agents and equip each one with structured outputs.

3.2

Run agents.

Run each agent on real inputs from your test scenarios, validating behavior before wiring up tools and orchestration.

3.3

Attach the tools you built in Phase 2 so agents can act on your systems.

3.4

Orchestrate agents.

Compose your agents into multi-agent workflows using either code-driven control flow or LLM-driven handoffs, depending on the reliability and flexibility you need.

3.5

Compose your agents into multi-agent workflows using either code-driven control flow or LLM-driven handoffs, depending on the reliability and flexibility you need.

Add session and memory management so agents persist context across turns, enabling coherent conversations and long-running multi-step workflows.

3.6

Implement guardrails and human-in-the-loop.

Implement agent-level and tool-level guardrails, content moderation via the Moderation API, and human-in-the-loop approval gates for high-risk tool calls.

Module 4: Evaluate & Optimize

The Purpose

You can’t optimize what you can’t see. Optimization is a loop: run > observe > evaluate > refine. Evals and observability aren’t optional, they’re how you make progress. Every run should be instrumented to show what the agent saw, what it did, where it failed, how long it took, and what it cost.

Key Benefits Achieved

An observable, measured, and optimized prototype.

Activities

Outputs

4.1

Implement tracing and observability.

Set up observability for your agents using the SDK's built-in tracing, custom traces for app-specific events, and run hooks for cross-cutting logic and external integrations.

4.2

Run evaluations.

Decide between programmatic and LLM-driven evaluation, then build and run evaluation sets against your traced agent runs to score quality and surface regressions.

4.3

Optimize agents.

Turn evaluation results into an optimization flywheel and maintain an experiment log to track changes, A/B comparisons, and quality gains over time.

4.4

Establish agentic AI FinOps.

Measure the costs associated with your agentic solution and compare to existing baselines to estimate ROI.

Module 5: Document & Showcase

The Purpose

A prototype becomes valuable when leadership can assess it. Clear evidence on performance, cost, and reliability is what turns a demo into an approved investment.

Key Benefits Achieved

A prototype demonstration with evidence pack that supports scaling decisions.

Activities

Outputs

5.1

Package the prototype.

Package the working prototype so it is reproducible and ready to demo. Finalize the repository structure, dependencies, environment configuration, and a runnable entry point.

5.2

Build a user interface.

Optionally add a lightweight UI on top of the agent so reviewers can interact with it directly and see inputs, outputs, and intermediate steps without reading code.

5.3

Summarize the implementation.

Summarize the implementation: the agents and tools built, the models powering them, and the governance and security controls applied across the system.

5.4

Document results and business impact.

Document the evaluation approach and the results: quality metrics, business impact, and the supporting evidence that justifies moving from prototype to pilot.

5.5

Identify blockers and next steps.

Identify the blockers preventing production readiness and define the next-step workstreams needed to close them, from integrations and guardrails to architecture and rollout.

5.6

Prepare the demo presentation.

Prepare the demo presentation: assemble the deck, plan the live walkthrough, and rehearse the agenda so stakeholders can clearly evaluate value and next steps.