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.

Develop Your Agentic AI Prototype

Develop agents with engineering best practices.

Develop Your Agentic AI Prototype

EXECUTIVE BRIEF

Executive summary

Your Challenge

Organizations that successfully design agentic AI prototypes often stall at implementation.
Development teams face unfamiliar tooling, nondeterministic system behavior, and integration complexity that traditional software development practices don't address.
Without engineering discipline, promising designs become fragile demos that can't survive real-world inputs, scale beyond a single developer's laptop, or provide the evidence leadership needs to approve investment.

Common Obstacles

Moving from design documents to working code without a reproducible development environment.
Building agents that work on happy paths but break unpredictably on edge cases.
Inability to measure what the system costs, how fast it runs, or where it fails.
Producing demos that impress but lack the evidence (evaluation results, cost projections, risk analysis) to justify production investment.
Knowledge silos – only the original developer can run or modify the prototype.

Info-Tech's Approach

Apply a disciplined build methodology that turns agentic AI designs into reproducible, defensible prototypes.
Follow a structured five-phase path – set up the stack, prepare data and tools, build agents, evaluate and optimize, then document and showcase.
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 – evaluation results, cost projections, and a demo – that gives leadership the proof they need to fund scaling.

Info-Tech Insight

Building a defensible agentic AI prototype requires five reinforcing phases: Set Up the Development Stack, Prepare Data & Build Tools, Build Agents, Evaluate & Optimize, and Document & Showcase. Together they move teams from fragile demo to defensible system with measurable business impact.

Your challenge

Composable multi-agent systems have the potential to deliver significant business value by automating complex business processes end to end. IDC predicts that within three years, 40% of Global 2,000 organizations will adopt agentic AI, with successful implementations doubling productivity (IDC, 2025).

As interest in agentic AI grows, IT leaders face increasing pressure to demonstrate value. In IBM's October 2025 survey of 800 C-suite executives, 38% of CIOs cited agentic AI as the year's most strategically important IT investment.

While interest and experimentation are high, many organizations struggle to move agents into production and realize ROI. Before moving to production, organizations need evidence that agentic AI solutions are feasible, viable, and secure within their business context. Prototyping is critical for generating this evidence, but the gap between agent design and a working prototype is larger than most teams expect.

Translating agent designs into running code introduces new challenges: nondeterministic behavior that complicates debugging, fragile tool integrations, orchestration patterns that compound errors across agents, and hidden cost dynamics that only emerge at runtime.

24%	of executives say AI agents take independent action in their organization today.
67%	of executives expect AI agents will take independent action by 2027.

Source: IBM, 2025

Barriers making it difficult to realize value

Due to the emergent nature of agentic AI, many development teams lack a disciplined approach for translating approved designs into code.

Engineering agentic AI systems requires supplementing traditional software development patterns with new patterns to address challenges such as unclear build sequencing, orchestration complexity, limited observability into model reasoning, and the absence of standardized evaluation discipline. Common failure patterns include:

Environment Fragility: Without reproducible, containerized environments, prototypes break when they move between developers, demo rooms, or CI pipelines.
Happy-Path Optimism: Agents work in demos but fail on edge cases. Without structured error handling, retry logic, and failure-mode testing, prototypes remain brittle.
Invisible Costs: Token spend is opaque until instrumented. Teams discover cost problems after deployment rather than during development.
Evaluation Theater: Running a demo is not an evaluation. Without rigorous, automated evals, teams can't objectively measure performance.
Security Gaps: Guardrails are implemented but never tested. Prompt injection, data exfiltration, and tool abuse scenarios go unexercised.

39%	of organizations are experimenting with agentic AI.
23%	of organizations have scaled an agentic AI system – but most only in one or two business functions.

Source: McKinsey & Company, 2025

Info-Tech's methodology to develop an agentic AI prototype

Product Requirements Document

	1. Set Up the Development Stack	2. Prepare Data & Build Tools	3. Build Agents	4. Evaluate & Optimize	5. Document & Showcase
Phase Steps	1.1 Determine agent development tech stack 1.2 Set up agent development tooling 1.3 Initialize agent development template 1.4 Responses API overview	2.1 Prepare input data for test scenarios 2.2 Initialize test scenarios 2.3 Build tools for system integration	3.1 Create agents 3.2 Run agents 3.3 Register agents with tools 3.4 Orchestrate agents 3.5 Add session/memory 3.6 Implement guardrails and human-in-the-loop	4.1 Implement tracing and observability 4.2 Run evaluations 4.3 Optimize agents 4.4 Establish agentic AI FinOps	5.1 Package the prototype 5.2 Build the user interface 5.3 Summarize the implementation 5.4 Document results and business impact 5.5 Identify blockers and next steps 5.6 Prepare the demo presentation
Phase Outcomes	A reproducible development environment and runnable repository	Agent-ready data and custom tools for each integration point	A runnable agentic system with guardrails and human-in-the-loop	An observable, measured, and optimized prototype	A prototype demonstration with evidence pack that supports scaling decisions

Insight summary

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.

Skip the setup and every later phase pays the tax

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.

The real engineering is in the integrations

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

Without rigorous engineering, agents improvise

No orchestration, no guardrails, and no memory management will result in unpredictable behavior. The agent will "make it work," but in an inconsistent and ungoverned way.

You can't optimize what you can't see

Optimization is a loop: run > observe > evaluate > refine. Evaluations 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.

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.

Key deliverables

You'll leave the blueprint with two artifacts: a runnable agent prototype and a stakeholder-ready demo deck. Together they prove the agent works and identify what it costs and where the risks sit, providing everything leadership needs to greenlight production.

Supporting tools & templates:

Coding tutorials
Development template
End-to-end examples
Demo presentation template

A working prototype

A runnable agent system built around your use case, with guardrails, human-in-the-loop checkpoints, and session memory wired in end to end.

A demo presentation

A presentation that showcases the agent in validated scenarios alongside evaluation results, risk analysis, cost indicators, and ROI projections – everything leadership needs to greenlight production.

Blueprint benefits

IT Benefits	Business Benefits
A reproducible, version-controlled development environment that any team member can run A working prototype that demonstrates real capability Instrumented observability with per-agent tracing, cost tracking, and latency measurement Tested guardrails with adversarial scenarios, not just happy-path validation A structured evaluation framework that produces measurable, comparable results across iterations Documented architecture and tooling decisions that support handoff to production teams	Evidence-based cost projections that finance teams can evaluate Clear documentation of what works, what doesn't, and what's needed for production Reduced time to pilot through a structured, repeatable development methodology Team capability building – developers emerge with hands-on agentic AI engineering skills

IT Benefits

Business Benefits

A reproducible, version-controlled development environment that any team member can run
A working prototype that demonstrates real capability
Instrumented observability with per-agent tracing, cost tracking, and latency measurement
Tested guardrails with adversarial scenarios, not just happy-path validation
A structured evaluation framework that produces measurable, comparable results across iterations
Documented architecture and tooling decisions that support handoff to production teams

Evidence-based cost projections that finance teams can evaluate
Clear documentation of what works, what doesn't, and what's needed for production
Reduced time to pilot through a structured, repeatable development methodology
Team capability building – developers emerge with hands-on agentic AI engineering skills

Info-Tech offers various levels of support to best suit your needs

DIY Toolkit

"Our team has already made this critical project a priority, and we have the time and capability, but some guidance along the way would be helpful."

Guided Implementation

"Our team knows that we need to fix a process, but we need assistance to determine where to focus. Some check-ins along the way would help keep us on track."

Workshop

"We need to hit the ground running and get this project kicked off immediately. Our team has the ability to take this over once we get a framework and strategy in place."

Executive & Technical Counseling

"Our team and processes are maturing; however, to expedite the journey we'll need a seasoned practitioner to coach and validate approaches, deliverables, and opportunities."

Consulting

"Our team does not have the time or the knowledge to take this project on. We need assistance through the entirety of this project."

Diagnostics and consistent frameworks are used throughout all five options.

Guided Implementation

What does a typical GI on this topic look like?

Guided Implementation

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is 8 to 12 calls over the course of 4 to 6 months.

Develop Your Agentic AI Prototype Workshop Agenda

	SESSION 0 Prototype Confirmation	SESSION 1 Base Prototype Development	SESSIONS 2 & 3 Two-Day Hackathon for Prototype Expansion & Validation	SESSION 4 Prototype Showcase
WHO	Leaders	Info-Tech Powered (1 week)	Technical Teams (2 Days)	Leaders and Technical Teams (1 Day)
ACTIVITIES	Review and validate Product Requirements Document (PRD) Provide synthetic data to Info-Tech Identify participants Schedule engagement	Develop a functional base prototype aligned to business needs, using the agreed-upon requirements within agent PRD Prepare clear technical documentation and learning materials to support understanding and future development Integrate essential features such as monitoring, safety controls, cost management, and benchmarking to ensure robust and reliable performance Identify and plan flexible integration points ("hooks") so the solution can be easily customized	Kick-off and orientation review of PRD Guided walkthrough of base prototype: Architecture and workflow design Key decisions, safety, and guardrails overview Observability, testing, token management, and FinOps/cost controls Hackathon: Block 1: Complete agent development; equip agents with tools; validate end-to-end workflow scenarios Block 2: Integrate system guardrails Block 3: Run evaluations; optimize quality, reliability, and performance Block 4: Walk through FastAPI implementation Showcase preparation	Deliver an executive-ready demonstration of the agentic AI solution, including business context, technical overview, governance controls, and measurable outcomes Showcase live prototype capabilities Present evaluation findings across performance, accuracy, cost efficiency, and latency Review implementation considerations and recommend next steps
OUTCOMES	Participant roles and responsibilities Prototype-ready data Approved PRD	A working prototype tailored to your requirements and ready for hands-on exploration and production concepts Comprehensive documentation and support material	Finalized agent prototype customized to best meet business and technical requirements	Stakeholder alignment on prototype outcomes and next steps

This research covers Step 2 in Info-Tech's agentic AI prototyping series. Leverage Info-Tech's Design and Develop workshops to rapidly go from vision to a working AI prototype.

Contact your account representative for more information.
workshops@infotech.com
1-888-670-8889

Workshop 1: Design Your Agentic AI Prototype

Design an AI agent that aligns business and technical requirements, is scalable, and is ready for development. In this workshop, your team will gain the skills to map business needs into a production-ready PRD with agent capabilities, orchestration patterns, guardrails, and clear evaluation criteria.

Workshop 2: Develop Your Agentic AI Prototype

Translate your vision into a working AI agent while acquiring essential strategies, hands-on skills, and a deeper understanding of what it takes to build successful AI agents.

Design Your Agentic AI Prototype Workshop Agenda

	SESSION 0 Use Case Alignment & Readiness	SESSION 1 Business Requirements & Value Alignment	SESSION 2 Map Your Agent Capabilities & Workflow	SESSION 3 Define Your Prototype Orchestration & Governance	SESSION 4 Define Your Agent Evaluation Criteria
WHO	Leaders	Leaders, Business Stakeholders, Technical Teams	Business Stakeholders, Technical Teams	Business Stakeholders, Technical Teams	Business Stakeholders, Technical Teams + Leaders
ACTIVITIES	Initial use case Documented workflow Early AI prototyping readiness assessment Schedule engagement	Introduction to agentic AI concepts Define the core problem statement Discover key user personas Document the business KPIs with baselines and targets Map the current-state workflow for the selected use case, and identify reasoning steps and edge cases Finalize the prototype scope and boundaries	Introduction to agent workflow design, models, tools, and instructions OpenAI Developer Crash Course 1 (Responses API, agents, tools, structured output) Identify the optimal model for each agent Define all necessary tools and agent instructions for each agent Optimize and rationalize agent distribution	Introduction to agent orchestration, guardrails, and human-in-the-loop (HITL) OpenAI Developer Crash Course 2 (orchestration, guardrails, observability, FinOps) Determine the optimal orchestration pattern for the use case Identify input, agent, and output risks Document all necessary guardrails and HITL steps	Introduction to agent evaluation OpenAI Developer Crash Course 3 (evals) Determine agent competencies, success criteria, and metrics Document agent tracing requirements Build evaluation datasets to test agents and the system Determine your experimentation plan and define next steps
OUTCOMES	Agreed-upon high-value use case Clear understanding of readiness	Use case problem statement, personas and KPIs Shared understanding of as-is workflow with reasoning steps and edge cases Shared understanding of prototype scope	Agent workflow design Model shortlist for each agent Data and tooling plan for each agent Draft instructions for each agent	Optimized agent workflow design Risk inventory Input, agent, and output-level guardrails and HITL	Agent success criteria, metrics, and tracing requirements Evaluation datasets Finalized PRD

Design your agentic AI workflow before starting this blueprint

Before you begin

As a prerequisite to this blueprint, you must have completed the Design Your Agentic AI Prototype blueprint, which covers selecting a use case, designing your agentic workflow, and documenting your design decisions in a product requirements document (PRD).

If you have not completed the Design Your Agentic AI Prototype blueprint or you do not have a PRD for your use case containing all the components on the right, you are not ready to work on this blueprint. Complete Design Your Agentic AI Prototype and then start this blueprint.

Download Info-Tech's Design Your Agentic AI Prototype

A complete product requirements document (PRD) will include:

Problem statement
KPIs with baselines & targets
Current-state workflow map
Defined prototype scope
Agentic workflow design
Models, tools & instructions
Orchestration pattern
Guardrails & risk controls
Human-in-the-loop
Success criteria
Tracing & observability criteria
Test scenarios

Pro-code vs. low-code

Building agents in a drag-and-drop platform gets you to a demo fast. Building them in code gets you to production.

Low-code trades flexibility for convenience. If your use case fits the platform's patterns, the speed advantage is real. But the moment you need custom orchestration, adversarial testing, fine-grained cost control, or production-grade observability, you'll outgrow the platform (and migration costs are steep).

This blueprint uses a pro-code development approach. The goal isn't just a demo – it's a prototype with the evidence pack (eval results, cost data, latency benchmarks) needed to justify production investment. That level of instrumentation, testing rigor, and architectural control requires code.

Dimension	Pro-Code (Python + SDK)	Low-Code (Drag and Drop)
Flexibility	Full control over agent logic, orchestration, prompts, and tool integrations.	Constrained to prebuilt components; custom logic requires workarounds or exits to code.
Customization	Every behavior is programmable – guardrails, evals, cost hooks, memory strategies.	Limited to platform config options; advanced customization hits a wall.
Integration	You own every connection – more work, but no vendor lock-in.	Prebuilt connectors for common systems; unsupported systems leave you stuck.
Debugging & Observability	Full access to logs, traces, execution state – instrument anything.	Limited to what the platform exposes; closed-box execution complicates root-cause analysis.
Portability	Code runs anywhere – local, cloud, CI/CD, air-gapped.	Tied to the platform; migration means rebuilding, not redeploying.
Speed to First Demo	Slower setup – dev environment, SDK knowledge, coding effort.	Faster – visual builders lower the barrier to a working prototype.
Team Skill	Requires Python proficiency and software engineering practices.	Accessible to nondevelopers or teams with limited coding experience.

Understand the repositories provided with this research

Five repositories support this research, organized into three types:

green: Coding tutorials, Blue: Development Template, Purple: End-to-End examples

Understand the use case referenced in the repositories

The repositories feature examples from a real service desk agentic AI system.

PROBLEM STATEMENT

The service desk triages thousands of tickets per month. The current process is manual, time consuming, and inconsistent. Agents must classify and route tickets across a large category taxonomy while working from unclear descriptions, incomplete information, and weak subject lines. This leads to long assignment times, avoidable back-and-forth with requestors, and inconsistent routing outcomes.

The workflow is also burdened by irrelevant tickets that belong outside the service desk and repetitive ticket types such as Database Integrity Reports, which consume capacity that should go to higher-value work. Because ticket content is highly variable and often ambiguous, traditional rules-based automation is not sufficient to automate classification, prioritization, routing, and requester follow-up reliably.

AGENTIC SOLUTION

Ticket Templating Agent
Determines if the ticket is a service request or incident report and selects the correct template.
Ticket Classification & Prioritization Agent
Categorizes the ticket by category, subcategory, and item and sets the ticket's priority.
Ticket Routing Agent
Routes the ticket to the correct team or technician based on capacity, availability, and topic.
Ticket Validation Agent
Requests additional information from the ticket submitter as needed.
Ticket Resolution Agent
Forwards irrelevant tickets and resolves Database Integrity Report tickets.

Phase 1

Set Up the Development Stack

Phase 1	Phase 2	Phase 3	Phase 4	Phase 5
1.1 Determine agent development tech stack 1.2 Set up agent development tooling 1.3 Initialize agent development template 1.4 Review the Responses API	2.1 Prepare input data for test scenarios 2.2 Initialize test scenarios 2.3 Build tools for system integration	3.1 Create agents 3.2 Run agents 3.3 Register agents with tools 3.4 Orchestrate agents 3.5 Add session/memory 3.6 Implement guardrails and human-in-the-loop	4.1 Implement tracing and observability 4.2 Run evaluations 4.3 Optimize agents 4.4 Establish agentic AI FinOps	5.1 Package the prototype 5.2 Build the user interface (optional) 5.3 Summarize the implementation 5.4 Document results and business impact 5.5 Identify blockers and next steps 5.6 Prepare the demo presentation

Phase 1

Phase 2

Phase 3

Phase 4

Phase 5

1.1 Determine agent development tech stack

1.2 Set up agent development tooling

1.3 Initialize agent development template

1.4 Review the Responses API

2.1 Prepare input data for test scenarios

2.2 Initialize test scenarios

2.3 Build tools for system integration