Prepare your business to build AI agents that get real work done.
Agents need more than capable models to deliver accurately, reliably, and in a governed manner. They need to understand the roles, rules, processes, and knowledge of your organization. In this live session, you will learn how to choose the right problems for agents, give them the context to perform, and move from experiment to execution. No coding background is required.
AI Agent: works toward a defined goal through permitted steps.
Agents need more than access to AI.
AI agents are opening new opportunities to improve productivity, streamline workflows, and carry out complex tasks. Building one that performs reliably takes more than powerful technology. An agent has to understand the work it is asked to do, including objectives, roles, processes, rules, and knowledge.
Without that context, even capable models produce results that vary from one run to the next. The gap between a promising experiment and a dependable agent is usually missing context.
- Agent ideas are coming from every team.
- Few people can name the first workflow worth building.
- A problem-first approach turns those ideas into a short, ranked list.
- Agents perform well in demos but vary in daily operations.
- Outputs drift because the agent lacks roles, rules, and process knowledge.
- Organizational context gives agents the same grounding your people rely on.
Agents perform when the work, the rules, and the people behind the work are defined. Agents 1.0 shows you how to define them.
Know which type of AI fits each job.
Respond to a person in the flow of their work. The person directs each step and reviews every output.
Best for: drafting, research, and analysis
Run a defined workflow when a trigger occurs. Each step follows fixed rules, so the result is predictable.
Best for: routing, data handling, and repeatable approvals
Work toward a goal by choosing steps, using tools, and checking progress. Guardrails and approval points set the limits.
Best for: multi-step work that changes from case to case
Three parts. One working agent plan.
Understand agents and where they add value
- How agents work and how they differ from traditional AI tools
- Where agents reduce repetitive work and support decisions
Give agents the context and structure they need
- Why organizational context drives agent performance
- Instructions, knowledge, tools, workflows, and guardrails
Move from experiment to execution
- Choose the right problem, then build, test, and improve
- Make agents a practical part of everyday operations
Start with the problem. Build the context. Put AI agents to work.
Set up the foundation once. Reuse it across every agent.
Agents built one at a time create one-off solutions. Shared infrastructure lets each new agent draw on the same organizational context, tools, and controls, so every team builds on the work of the last.
- Shared context blocks for roles, processes, and rules
- Common tool connections and access permissions
- One set of guardrails and approval points
- Measures that show how each agent performs
Bring the problem. The agent comes after.
Built for the people who will work with agents.
- Business leaders exploring AI opportunities
- Operations and functional managers looking to improve workflows
- AI transformation and innovation teams
- IT and technology leaders supporting AI implementation
- Professionals interested in building AI agents for their own roles
No coding or technical background is required. The session focuses on the practical foundations of building agents around real business needs.
Build the capability to put AI agents to work.
Join The Kendall Project for Agents 1.0 and take the first steps toward agents that understand your business and deliver meaningful results.
Register for Agents 1.0
Complete the form to reserve your place in the live virtual webinar on Tuesday, December 15, 2026.