Organizations recognize AI agents as essential for driving innovation and competitive advantage, but initiatives often stall due to misalignment, complexity, and uncertainty.
- Agentic use cases are selected without a clear understanding of desired outcomes, and don’t deliver value. As a result, the agent may technically work, but it doesn't drive real ROI or reduce workload in areas that matter.
- Poor workflow and capability design results in increased agent complexity and failure to scale and requires constant supervision.
- No orchestration or governance design means that risk, inconsistency and trust issues emerge as agents go into production.
- Lack of evaluation frameworks means that organizations can’t prove impact, learn fast, or justify further investment.
Leverage this approach for a faster, more focused path from AI ideas to real agentic AI prototypes. Design the foundations upfront so teams can move quickly without misalignment, rework, or risk.
- Accelerate time-to-value by ensuring the best use case is selected so teams invest in the opportunities that matter.
- Design your agentic AI systems for real impact with explicit alignment to business outcomes, personas, KPIs, and constraints that guide all design decisions.
- Reduce delivery of risk and rework by designing agent workflows, orchestration, guardrails, and human oversight intentionally before development begins.
- Provide your developers with meaningful training on how to build agents in OpenAI SDK.
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Onsite 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 onsite delivery of our Project Workshops. We take you through every phase of your project and ensure that you have a road map in place to complete your project successfully.
Book NowModule 1: Define Business Requirements & Align on Value Proposition
The Purpose
Convert business needs into a clear problem statement, success criteria, and scope to ensure a shared definition of “value,” which must inform every design decision.
Key Benefits Achieved
- Clear line of sight between agent opportunities and measurable business impact.
- Defined personas, KPIs and identified constraints that will ensure your agentic AI system will deliver value.
- Finalize your agentic AI prototype scope across business stakeholders and technical teams.
| Activities: | Outputs: | |
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| 1.1 | Introduction to agentic AI concepts |
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| 1.2 | Define the core problem statement |
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| 1.3 | Discover key user personas |
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| 1.4 | Document business KPIs with baselines and targets |
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| 1.5 | Map the current-state workflow for the selected use case, identifying reasoning steps and edge cases |
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| 1.6 | Finalize the prototype scope and boundaries |
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Module 2: Map Your Agent Capabilities & Workflow
The Purpose
Design how your agents will work, including mapping workflows, decisions, tools, and handoffs between humans and agents.
Key Benefits Achieved
- Visualize your agentic workflow to demonstrate how your agents will function.
- Identify the right models, tools, and instructions for each agent.
- Prepare your developers to build APIs, agents, tools, and outputs in OpenAI.
| Activities: | Outputs: | |
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| 2.1 | Introduction to agent workflow design, models, tools, and instructions |
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| 2.2 | OpenAI Developer Crash Course 1: APIs, agents, tools & structured output. |
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| 2.3 | Identify the optimal model for each agent |
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| 2.4 | Define the necessary tools and agent instructions for each agent |
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| 2.5 | Optimize and rationalize agent distribution |
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Module 3: Define Your Prototype Orchestration & Governance
The Purpose
Define how agents are orchestrated, governed, and observed by embedding accountability and human oversight by design.
Key Benefits Achieved
- Design agent orchestration with clear controls, guardrails and oversight.
- Clearly identify areas for guardrails and human-in-the-loop requirements.
- Prepare your developers to build guardrails and orchestration patterns in OpenAI.
| Activities: | Outputs: | |
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| 3.1 | Introduction to agent orchestration, guardrails, and human-in-the-loop (HITL) |
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| 3.2 | OpenAI Developer Crash Course 3: Orchestration, guardrails, observability, FinOps |
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| 3.3 | Determine the optimized orchestration pattern for the use case |
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| 3.4 | Identify input, agent, and output risks |
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| 3.5 | Document all necessary guardrails and HITL steps |
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Module 4: Define Your Agent Evaluation Criteria
The Purpose
Establish clear evaluation criteria including metrics, test cases, traceability, and security.
Key Benefits Achieved
- Define what good looks like through clear agent success metrics.
- Establish your evaluation datasets and test criteria, and ensure design traceability.
- Set realistic expectations around next steps for the design finalization and prototype build.
- Prepare your developers to perform evaluations in OpenAI.
| Activities: | Outputs: | |
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| 4.1 | Introduction to agent evaluation |
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| 4.2 | OpenAI Developer Crash Course 4: Evaluations |
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| 4.3 | Document agent competencies, success criteria, and metrics |
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| 4.4 | Document agent tracing requirements |
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| 4.5 | Build evaluation datasets to test agents and the system |
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| 4.6 | Determine your experimentation plan & define next steps |
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