We’re starting with one of the most overlooked (but critical) pieces of enterprise agent design: clear, structured instructions.

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Even the most advanced models can underperform if the instructions are vague, incomplete, or disconnected from real user needs. If you’ve seen agents give inconsistent or generic answers, the issue usually isn’t the model—it’s the missing blueprint.

That’s why I developed a practical AI Agent Instructions Framework. This helps teams build agents that are accurate, useful, and aligned with how real people work.

Step 1: Gather Prompts and Requirements Using a Simple Table

Before defining any logic or connecting APIs, start by capturing what the user needs, what a great answer looks like, and where the agent should go to find that information.

Use this structure:

This table gets everyone aligned: business users, designers, and developers.

What this enables:

• Ensures accuracy by defining clear expectations and outputs

• Improves retrieval by specifying the right knowledge source

• Builds user trust by reducing hallucination or incorrect answers

• Speeds development by removing ambiguity from the design phase

Want to see how I use this table in real-world builds? [Watch the YouTube walkthrough video]

Step 2: Use the Template to Define Your Agent Instructions

Once your inputs are clear, build out the full instruction set using this structure:

Agent Purpose

Briefly describe what this agent does and who it serves.

Example: The Procurement Agent helps supply chain managers validate purchase requests, suggest preferred suppliers, and flag potential exceptions before approval.

Core Functions

Break down what the agent must do. For each function:

• What it does: Describe the purpose

• Key actions: List tasks or workflows

Example:

Function: Supplier Identification

What it does: Helps users identify preferred or alternate suppliers for a given product

Key actions:

• Search vendor database for approved suppliers

• Check for active contracts

• Recommend top 3 options with contract terms

Repeat for each function.

What’s Next: Free to Start, Built to Go Deep

Thanks for reading the first edition of my AI newsletter and will continue after this. To kick things off, this post is free and open to all—because I want you to see the value upfront.

But this is just the beginning.

In the coming issues, I’ll be sharing deeper, more exclusive content for paid subscribers—covering everything from:

• How I design and test AI agents across Microsoft Copilot, Dynamics 365, Gemini, Claude, and ChatGPT

• Real-world enterprise use cases with agent blueprints and outcomes

• Downloadable instruction templates, architecture guides, and hands-on walkthroughs

• Audio versions and premium strategy explainers

Whether you’re building for yourself or scaling AI across a team, this newsletter is here to help you think bigger—and execute smarter.

To get it all, please subscribe. I’m excited to share more with you.

Knowledge Architecture

Define how the agent retrieves and uses knowledge:

• Instruction Repository: Templates, policy docs, internal playbooks

• Search & Retrieval: Structured access to internal systems (e.g., SharePoint, Dynamics, Knowledge Base)

• Other References: Naming conventions or required file structures

Interaction Framework

Explain how the agent handles user queries and responses:

• Use plain language and clear steps

• Guide the user when decisions are involved

• Examples of question + response

• Reinforce confidence in results and offer follow-up

Feedback Loop

Include a way for users to suggest improvements or flag inaccurate answers.

Example Use Cases

Illustrate how the agent helps in different scenarios:

• Use Case 1: A regional manager requests updated pricing for a high-volume item

• Use Case 2: A finance analyst needs documentation for a late invoice exception

• Use Case 3: A warehouse team lead asks about safety protocols for a new shipment type

Handling Edge Cases

Anticipate what happens when things don’t go as planned:

• No Exact Match: Provide best alternative + explain

• Multiple Matches: Rank by relevance, then list options

• No Data Found: Inform user + suggest next steps or escalation

Final Notes

Include any guardrails, priorities, and formatting expectations:

• Prioritize internal policy over general web data

• Clearly label approximations

• Reference official documentation where available

Reference Documents

List key sources:

• “Procurement Policy v2024” – Internal rules for vendor selection

• “Invoice Exception Handling SOP” – Step-by-step guidance for AP teams

• “Finance Onboarding Checklist” – HR and training resources

What the Agent Should Not Do

• Do not provide generic, vague responses

• Do not invent answers or suggest unsupported actions

• Do not handle sensitive or personally identifiable data

• Do not bypass required approval processes

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In Closing

This framework isn’t just a checklist—it’s the foundation for scaling trustworthy AI inside the enterprise. With this structure, you’re not just building a chatbot—you’re deploying a digital teammate that actually understands your business.

In the next issue, I’ll walk through how to test and improve agent instructions with real feedback loops, telemetry, and iterative fine-tuning.

Thanks for reading,

Ragnar Pitla