Lesson 2.3 – Requirements & Risk Planning
Designing a Solid Foundation Before You Build
📌 Introduction
Before you write a single prompt or upload a document, it’s essential to take a step back and ask: “What does this AI Agent actually need to do, and what could go wrong?”
AI Agents aren’t traditional software systems — but they still need clear requirements, thoughtful planning, and proactive risk mitigation.
This lesson will guide you through gathering the functional and non-functional requirements for your agent, scoping data and integration needs, and identifying risks and roadblocks before they disrupt your project.
📘 “Most failed AI Agent projects didn’t fail in production — they failed in planning.”
🧠 What’s Different About AI Requirements?
Traditional software requirements are detailed, rigid, and focused on logic. AI Agent requirements are flexible, iterative, and context-dependent.
You’ll need to capture:
What kind of tasks the agent must handle
What training data it will need to reason effectively
What systems it must connect to
What level of autonomy and risk tolerance is acceptable
Who owns ongoing improvement and testing
🧩 Phase 1: Requirements Gathering
Break your planning into functional, non-functional, and business requirements.
✅ Functional Requirements (What the Agent Does)
Use user stories to define this.
Template:
As a [user type], I want the agent to [do something], so that [business benefit].
Examples:
As a support rep, I want the agent to suggest answers from our knowledge base
As a sales manager, I want the agent to qualify leads and schedule follow-ups
As an HR coordinator, I want the agent to answer common policy questions for employees
📘 Use this to identify the core capabilities your agent must have — and the logic behind them.
✅ Non-Functional Requirements (How the Agent Performs)
These define performance expectations and system qualities.
Response Time
Must reply in < 2 seconds for live chat
Availability
99% uptime for internal copilot use
Accuracy Target
85%+ accuracy as rated by users
Security
No PII stored in logs, SSO required
Scalability
Should support 5–50 concurrent users
Language/Tone
Casual tone for HR, formal tone for finance
📘 “Treat tone, style, formatting, and escalation logic as requirements too — not afterthoughts.”
✅ Stakeholder Mapping
List the people and roles involved in development, deployment, and feedback.
Project Owner
Defines scope and signs off
Business Lead
Provides training materials, validates output
Technical Lead
Manages vector store, integrations, and agent config
QA / Tester
Runs test cases and collects feedback
Compliance
Reviews sensitive data handling
🧬 Phase 2: Data and Integration Planning
This is where most of your work will happen.
🗃️ Data Requirements
What are the source systems?
SharePoint, Zendesk, Docs, Email Archives
What format is content in?
PDFs, HTML, Word, JSON
Is the data complete and current?
If not, it must be curated
Who owns the data prep?
Assign a curator, use raia Academy
How will you maintain the data?
Set update frequency (weekly, monthly, etc.)
🔗 Integration Requirements
What systems must the agent read from?
CRM, Knowledge Base, Internal APIs
What systems must it write to or trigger?
Create tickets, update contacts, send alerts
What authentication is required?
API keys, OAuth, role-based permissions
Will you use n8n workflows?
For multi-step orchestration or cross-system logic
What happens if a workflow fails?
Error messages, retry logic, escalation
📘 “Don’t build complex automations on Day 1. Prove retrieval + reasoning first, then automate.”
⚠️ Phase 3: Risk Assessment & Mitigation
Identify common risks and build a plan before deployment.
🔍 Common Risk Areas
Poor Accuracy
Weak training data or chunking
Use raia Academy to audit and fix
Hallucinations
Missing source info or poor prompt design
Refine system instructions, expand vector store
Data Exposure
Sensitive info retrieved accidentally
Apply access controls, document tags, compliance review
Integration Failures
Bad API responses, expired tokens
Build test coverage and alerting
Low Adoption
Users don’t trust or understand agent
Provide training, explain limitations early
Misaligned Expectations
Users assume perfection
Set realistic expectations, emphasize iteration
🛠 Risk Assessment Template
Missing or outdated training content
4
5
20
Audit source docs, update monthly
Users don’t trust agent output
3
4
12
Involve users in testing, surface sources
API integration fails
2
5
10
Use n8n with fallback and retries
Agent gives wrong answers
4
4
16
Feedback loop via raia Copilot
🗓️ Phase 4: Project Timeline (12-Week Sample)
1–2
Finalize requirements, assign roles, prep initial documents
3–4
Transform data, upload to vector store, configure agent
5–6
Add integrations, test workflows, tune prompts
7–8
Run internal pilot (Copilot), gather feedback
9–10
Improve based on usage, test again
11–12
Prepare launch plan, train users, go live
📘 “Treat your AI Agent like a new employee — you don’t throw them into production without training and orientation.”
✅ Key Takeaways
AI Agents need clear, flexible requirements: what they should do, how they should behave, and what they should connect to
Define both functional and non-functional needs — from tone to accuracy expectations
Plan for training data preparation and integration complexity upfront
Use a risk matrix to anticipate and address issues before launch
Involve stakeholders early, assign owners, and map the project timeline from the start
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