How to Run a Five-Day AI Agent Sprint for Your Enterprise

Running an AI agent sprint isn't about jumping straight into code. It's about mapping opportunities, building a business case, and prototyping something that actually works. Here's the complete playbook for completing a five-day sprint that finishes with working prototypes and executive buy-in.

How to Build the Map of Opportunities

The first step is a 60-minute meeting to uncover the biggest problem areas where agents could help. You're also trying to understand the business's core systems and data.

The goal here is finding every use case, re-imagining the work, and building champions. Your output should be a target list of 30 to 60 use cases. Size the opportunity around 10 to 20 of them. Design 3 to 4 agents. Identify one champion per function.

Where to look:

• Cost structure: Rank teams by headcount and spend. The biggest teams usually hide the biggest agents.
• Org chart: For every team over about 5 people, ask what comes in and what goes out.
• Follow the work: Trace one invoice, hire, or ticket across every team that touches it.
• Queues and recurring work: Shared inboxes, ticket and approval queues, weekly reports, and standing meetings.
• Surveys and old AI intake lists: If your org has them.

Two Days to Audit and Prioritize the Business Case

Over the next two days, you're building a strong ROI case for each opportunity and prioritizing by the biggest opportunities that can be delivered soonest. Before you begin, make sure you've completed an enterprise AI deployment checklist to understand what needs to work before agents reach production.

The business case breaks down into three areas:

• Labor: Annual hours times percentage of time removed times loaded hourly cost. Call it capacity returned, not cash. And say how it becomes cash: fewer hires, less contractor or BPO spend, or people moved to revenue work.
• Financial: Faster collection (amount collected sooner times cost of capital), less bad debt, recovered revenue, lower spend, avoided BPO cost, and captured discounts.
• Quality and risk: Volume times error rate times cost per error times percentage prevented. Usually the softest number, so never let it carry a case alone.

Connect Your Systems Through a Data Gateway

All public MCP, OpenAPI, and GraphQL systems get set up instantly. Custom or private connectors get mocked out for the sprint. Use a gateway product to wire up all key systems, building custom connectors or MCP servers if necessary.

Give the agent a map of your data and organization:

• Data map: The tables or objects that matter, what each holds, key fields, join paths, standard filters, and field meanings in plain words
• Query recipes: Tested queries or API calls for the agent's common questions
• Ignore list: What looks relevant but isn't, and why
• Organization map: Business units, clients, retailers, accounts, company codes and cost centers, and how they relate
• Aliases: How people name things, mapped to system IDs
• Glossary: Your company's terms and acronyms
• Gotchas: Known traps

One Day to Define the Agent Behavior

This is where you write out the agent's core behavior and build up its set of capabilities as skills with a skill router as the entrypoint.

Here's the order of operations:

• Pin the outcome the agent owns
• Map today's process: trigger, steps, people, systems, decisions, approvals, exceptions, volume, and time
• Re-imagine it from first principles
• Brainstorm 10 to 20 capabilities
• Shape behavior: router, skills, constitution, autonomy levels
• Map connections and listeners
• Draft the context blueprint
• Size the business case with the Business Case Method
• Script the golden demo
• Review with the workflow owner

Prototype and Test the Agent Internally

Over one day, run the agent's core workflows in Slack or Teams, upgrading the agent to a custom web app if necessary.

The principles that matter:

• Write the golden demo before writing code. The script decides what to build and what data to seed.
• Emulate, don't integrate. No production systems or live credentials.
• Real on the surface, synthetic underneath. Their names, templates, and terms, with your data.
• Same path as production. Agents call emulators through data gateway, so going live means swapping the connection, not rebuilding.
• Demo path first. Get the golden demo running end to end before adding anything else.

Present, Iterate, and Polish

After prototyping, you'll run through an initial demo with your internal champion in a one-hour session. Transcribe all feedback. Then take one more day to make key changes and polish the agent's behavior until it can consistently nail the golden demo.

What Makes a Demo Day Presentation Work

Demo day is a one-hour session where you present the agents you've prototyped and their business cases to executive stakeholders. You're also defining the longer-term roadmap.

Every demo must have these elements:

• Follows one protagonist for 5 to 8 minutes
• Starts with the agent reacting to an event, email, or schedule, not a prompt
• Handles a messy case
• Works in a real artifact and shows it
• Shows an approval enforced through data gateway
• Finishes the loop: send, log, follow up
• Uses next-step suggestions and the client's own language
• Ends on a business number tied to the business case

Five Days Gets You Working Prototypes

The sprint framework moves fast: one day to map opportunities, two days to build the business case, instant system connections, one day for behavior design, one day for prototyping, and demo day. By the end, you have up to 4 working agent prototypes with clear ROI cases attached.

The key is starting with the map, not the code. When you know where the biggest opportunities are and you've got champions in each function, the prototypes you build actually matter to the business.

If you want a free 5-day AI agent sprint for your business that finishes with up to 4 working prototypes, Tenex Labs runs these for select enterprises.