AI Agent Instructions Template: Build Smarter, Not Just Faster with Ragnar’s Framework
Your practical guide to writing clear instructions and building agents that actually work.
Welcome to the first edition of Ragnar’s AI Newsletter—your biweekly guide to navigating the AI-first enterprise era.
We’re starting with one of the most overlooked (but critical) pieces of enterprise agent design: clear, structured instructions.
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
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