• Organizations recognize AI agents as a huge opportunity for driving innovation and competitive advantage, but initiatives often stall due to misalignment, complexity, and a lack of understanding around what “agents” can really do.
• IT faces increasing pressure from leadership to quickly demonstrate tangible AI value makes delays costly, eroding stakeholder confidence and momentum.
• Early AI projects that fail to deliver clear, actionable results lead to skepticism, missed opportunities, and prolonged implementation cycles.

• Teams lack shared architectures for agent communication, orchestration, and evaluation. When teams build from scratch under these conditions, duplicated effort, inconsistent interfaces, and fragile prototypes emerge.
• Traditional benchmarks focus on accuracy or latency, not autonomy, reasoning depth, or coordination quality. Evaluating agents requires new metrics and methodologies.
• Enterprises need robust guardrails, observability, and optimization mechanisms before agents can be safely deployed at scale.

• Establish an agentic AI design framework for standardizing a prototype-to-production pipeline for AI agents.
• Design a product requirements document (PRD) that guides from prototype to production using our structured, rapid-prototyping process.
• 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 business value, and establish a scalable, repeatable approach to agent-based AI across the enterprise.

Assist: Human prompts the AI, which then drafts content or uses tools, often with RAG. The human retains control for execution. No autonomous ReACT cycle is completed by the AI.

Single-Agent Autonomy: A single agent plans and acts across various tools based on a human-set goal. Key steps may require human approval, but the agent drives the workflow; execution can be by human or agent.

Multi-Agent Autonomy: Multiple agents collaborate under an orchestrator to achieve complex business outcomes. This system leverages distributed intelligence for execution, with or without human intervention.

This LLM is enhanced with external capabilities but still works in a linear, input to output flow. The LLM only acts when prompted and its workflow is a single-pass interaction.

Process:

• Input is passed into the LLM.
• The LLM can call retrieval, tools, and memory as needed to enrich its response.
• Output is generated and sent back.

This LLM becomes part of a closed-loop system that allows autonomous decision-making and continuous action without constant human prompting.

Process:

• Input still flows into the LLM.
• The LLM now also interacts with decision and observation processing modules.
• These modules enable the system to evaluate results, adapt to environmental feedback, and iterate through multiple cycles before producing final output.

What an agent does:

• Gathers context across systems
• Evaluates trade-offs and exceptions
• Recommends decisions with rationale
• Escalates only when needed

How this helps:

• Faster, more consistent decisions
• Fewer edge cases handled manually
• Reduced workload on experts

Example: Refund approval in customer service workflows

What an agent does:

• Interprets policies instead of hard-coded rules
• Adapts instantly to changes
• Handles incomplete or ambiguous inputs
• Learns from past decisions

How this helps:

• Lower automation maintenance
• Faster response to policy changes
• Less technical debt

Example: Performing vendor security reviews

What an agent does:

• Understands documents and conversations
• Extracts key information and intent
• Drives multi-step workflows
• Communicates in natural language

How this helps:

• Less manual review and triage
• Faster end-to-end processing
• Better user experience

Example: Processing a home insurance claim

Logic flow

Predetermined if/then branches

Dynamic reasoning based on context

Input handling

Structured, validated fields

Natural-language understanding

Adaptability

Code changes required

Learns from instructions/examples

Error handling

Specific error codes

Contextual recovery strategies

User interaction

Forms, buttons, static APIs

Conversational and intuitive

Process Blindness

Attempting to automate a workflow that is poorly documented, inconsistent, or lacks a clear logic owner.

Instead: Document and map the “as-is” workflow and all its exceptions before the implementation.

Integration Gaps

Building an agent in a sandbox without verifying if it can access required APIs or databases in a live environment.

Instead: Conduct a technical "plumbing" check early to ensure the agent has authenticated access to every tool it needs.

Governance Too Late

Treating Security, Legal, and Compliance as a "final check," leading to projects being blocked right before launch.

Instead: Embed "Privacy by Design" and Human-in-the-Loop (HITL) checkpoints from the requirements phase.

Unclear “Good”

Failing to define what a "correct" answer looks like, making it impossible to measure if the agent is effective.

Instead: Establish success metrics (e.g. 90% accuracy on queries) and test datasets before deployment.

Lack of Observability

Running agents where you can see the final output but have no way to trace the steps the agent took to get there.

Instead: Implement comprehensive traceability from day one.

Cost Surprises

Failing to account for how "loops" and high token usage in complex agentic workflows can lead to exponential API costs.

Instead: Set strict token limits, "max-turn" guards, and real-time cost monitoring for all production agents.

1.1 Define your problem statement

1.2 Outline personas

1.3 Set KPIs

1.4 Map current workflow

1.5 Determine prototype scope

2.1 Design an agentic workflow

2.2 Select agent model(s)

2.3 Specify agent tools

2.4 Author agent instructions

2.5 Optimize agent distribution

3.1 Select the orchestration strategy

3.2 Identify risks

3.3 Define guardrails & human-in-the-loop

3.4 Design agentic workflow (with guardrails & human-in-the-loop)

4.1 Define success criteria & metrics

4.2 Plan for tracing & observability

4.3 Create evaluation datasets

4.4 Determine next steps

• Unified problem statement and clearly defined value proposition
• Current workflow with personas documented
• KPIs with baselines and targets established
• Agent scope outlined

• Optimal agent model(s) selected
• Comprehensive instructions finalized, including few-shot examples and output contract
• Supporting tool and data integrations defined

• Orchestration and safety framework with clear delegation and approvals
• Governed, auditable, production-safe agentic workflow design

• Complete evaluation plan including success criteria and metrics
• Defined testing approach and evaluation dataset
• Established observability and tracing requirements
• Selected criteria for production readiness and next-step recommendations

• Establish a scalable AI architecture, moving beyond ad hoc deployments to a module-based, extensive framework for agent-based systems.
• Introduce traceable agent behaviors and monitoring mechanisms for better system oversight.
• Measure and control AI actions across connected tools, which is a prerequisite for compliance and risk management.
• Benchmark and optimize compute, storage, and latency trade-offs before production rollout.
• Create the foundation for AIOps practices, for automated issue detection, rollback, and performance tuning.
• De-risk future AI deployments by experimenting in a controlled prototype phase.

• Transform AI from isolated pilots into a repeatable, scalable capability that shortens time from idea to impact.
• Enables teams to prototype, test, and deploy AI workflows faster by defining clear requirements, success metrics, and guardrails.
• Moves the organization toward continuous AI delivery, where new use cases can be launched with minimal friction.
• Standardized agent frameworks reduce redundancy and rework across teams and business units.
• Develop faster and better informed strategic decisions that are grounded in real-time data and contextual reasoning.
• Create a foundation for new AI-enabled products, services, or customer experiences.

Call #1: Define problem statement, personas & KPIs.

Call #2: Map current workflow and set prototype scope.

Call #3: Design agent workflow with models, tools, & instructions.

Call #4: Optimize agent distribution.

Call #5: Choose orchestration pattern.

Call #6: Design guardrails and human-in-the-loop.

Call #7: Define successful agent behavior & select success metrics.

Call #8: Create tracing & experimentation plan.

Call #9: Identify next steps and finalize documents & approvals.

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.

Leaders

Leaders, Business Stakeholders, Technical Teams

Business Stakeholders, Technical Teams

Business Stakeholders, Technical Teams

Business Stakeholders, Technical Teams + Leaders

• 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 & edge cases
• Finalize the prototype scope & boundaries

• Introduction to agent workflow design, models, tools, and instructions
• OpenAI Developer Crash Course 1 (API, agents, tools, structured output)
• Identify agent 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

• Agreed upon high-value use case
• Clear understanding of readiness

• Use case problem statement, personas & KPIs
• Shared understanding of as-is workflow with reasoning steps & 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 & HITL

• Agent success criteria, metrics, and tracing requirements
• Evaluation datasets
• Finalized PRD