Build Your Agentic AI Prototype

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AI agents are reshaping how work gets done. But most organizations are stuck in endless planning cycles. Leadership wants results, teams need clarity, and the window for experimentation is closing. Realize the potential of an AI agent with our practical prototyping approach that accelerates AI agent adoption, aligns stakeholders, and builds the foundation for scalable enterprise-wide success.

Organizations recognize AI agents as essential for driving innovation and competitive advantage, but initiatives often stall due to misalignment, complexity, and uncertainty. Accelerate your agentic AI results with a structured rapid-prototyping approach that guides you from initial concept through to a functional AI agent prototype.

1. Move quickly, win early.

Speed matters. AI is a fast-moving field where delays and failures can mean missing out on windows of opportunity to gain competitive advantage, improve efficiency, or enhance customer experience. Rapid prototyping can help cut through uncertainty and align stakeholders around real, testable outcomes.

2. Beware the shiny objects.

Prototyping for functionality may build organizational excitement, but prototyping for scalability, safety, and sustainability will build real-world value. Organizations that prioritize risk, governance, and cost control early have a better chance of turning pilots into production.

3. Iteration is the way.

Refine before you scale. Continuous feedback, benchmarking, and refinement tied to key metrics are essential to evolving agents from prototypes to production-ready systems – and to ensuring real value when deployed at scale.

Use this step-by-step approach to transform your agentic AI idea into reality

Deliver a functional agent your teams can use immediately to start creating real business value. This practical, step-by-step research can help you move from idea to a working prototype, helping to de-risk your AI journey and align stakeholders around real, testable outcomes.

Establish agentic AI foundations: Surface the strategic alignment and data gaps that make or break an AI agent before writing code.
Architect agent reasoning & experience: Wire up tools, RAG, memory, and prompts into a usable core agent.
Ensure safe autonomy & efficiency: Include guardrails, observability, and FinOps from day one.
Test & evolve your agent: Benchmark, gather feedback, and refine before you scale.

Build Your Agentic AI Prototype Research & Tools

1. Build Your Agentic AI Prototype Storyboard – A guide to unlocking the potential of AI agents.

Use this research to:

Implement a structured, rapid-prototyping approach from initial concept through to a functional AI agent prototype.
Embed governance, safety, observability, and cost management practices early to directly address stakeholder concerns and ensure operational alignment.
Leverage immediate, tangible results to drive consensus, demonstrate clear value, and establish a scalable, repeatable approach to agent-based AI across the organization.

Workshop: Build 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: Use Case Alignment & Readiness

The Purpose

Surface the strategic alignment and data gaps that make or break an AI agent before writing code.

Key Benefits Achieved

LLM capability sweet spots and limitations mapped for the use case.
Benchmark thresholds set for prompt quality versus latency trade-offs.
Data maturity gaps uncovered for critical knowledge sources.

Activities

Outputs

1.1

Initial use case/workflow discussion and scoping

Agreed upon high-value use case

1.2

Early AI readiness assessment

Clear understanding of readiness

1.3

Schedule engagement

Module 2: Define, Align & Learn Agentic AI Foundations

The Purpose

Build shared foundational AI/LLM knowledge.

Key Benefits Achieved

Review input/output modalities (text, code, data, integrations)
Analyze LLM benchmarks, select best-fit models, define KPIs
Discuss tool integrations (APIs, MCP, browser, search)
Intro to RAG & memory

Activities

Outputs

2.1

Introduce agentic AI concepts, LLMs, and prompting basics

Shared foundational AI/LLM knowledge

2.2

Revisit and confirm use case and goals

2.3

Co-build Agent Product Requirements Document (PRD)

Completed Agent PRD

Module 3: Base Prototype Development

The Purpose

Show value fast – wire up tools, RAG, memory, and prompts into a usable core agent.

Key Benefits Achieved

Running agent with APIs, browser/model context protocol (MCP) search, retrieval-augmented generation (RAG), and memory.
Prompt engineering patterns evaluated for accuracy versus cost.
Memory window and state-management scopes tuned for context persistence.
UX flows stress-tested against real stakeholder scenarios.

Activities

Outputs

3.1

Develop a functional base prototype aligned to business needs, using the agreed-upon requirements within Agent PRD

A working prototype tailored to your requirements and ready for hands-on exploration

3.2

Prepare clear technical documentation and learning materials to support understanding and future development

Comprehensive documentation and support materials provided to enable smooth adoption

3.3

Integrate essential features such as monitoring, safety controls, cost management, and benchmarking to ensure robust and reliable performance

3.4

Identify and plan flexible integration points (“hooks”) so the solution can be easily customized

Module 4: Hackathon – Build, Customize, Innovate

The Purpose

Improve with evidence – benchmark, gather feedback, and refine before you scale.

Key Benefits Achieved

Performance deltas quantified against internal and industry benchmarks.
User feedback themes triaged by business impact and implementation effort.
Prototype refinements tied to key metrics (accuracy, speed, cost).
Sprint-based rollout roadmap defined with scaling milestones.

Activities

Outputs

4.1

Guided walkthrough and demo of base prototype with provided technical documentation, including workflow architecture, design decisions, observability, testing, token management, safety, guardrails, and FinOps/cost controls

4.2

Hands-on hackathon: team adds custom agents, features, and integrations