You've just walked out of the meeting where your AI agent project got the green light.
The executives nodded, the budget was approved, and someone mentioned a "solution design document" as the next deliverable.
Now you're staring at a blank document, wondering how to turn "build an AI agent for logistics tracking" into something that actually guides development and deployment.
Here's what most people get wrong: they think solution design is about the clever AI bits—the model selection, the prompt engineering, the reasoning framework.
It’s not.
Enterprise solution design is about everything else: how it integrates, how it scales, how it fails, how it gets maintained, and how it behaves ethically under pressure.
The difference between a solution design that works and one that creates chaos isn't the sophistication of the AI—it's the thoroughness of the enterprise thinking.
Today I’ll show you some of the aspects I add to my documentation and how you can apply them for your projects.
Let’s get into it.
Why Most First AI Agent Designs Fail
There might be some of you out there that think design documents are a bit 90’s. The truth is, in the enterprise, they’re alive and well. The main reason being (other than the obvious of it being good practice to design things properly and document) that organisational governance demands it. Enterprise IT always has working groups and review boards for projects and without attention to this due diligence, your project, no matter what it is, is just a toy or a PoC.
When it comes to what works and what doesn’t, the failure patterns are predictable. Again, it doesn’t matter if it’s a web app or an agent, if you cannot document and present your solution in a cohesive manner to the stakeholders in your review cycle, you’ll not get the support you need from the technical community to implement.
There’s a few common points where agent designs run aground.
The Academic Paper Style
Detailed explanations of transformer architectures and attention mechanisms. No mention of how it connects to the CRM or what happens when the API goes down.
The Happy Path Design
Everything works perfectly, users ask reasonable questions, and the agent always has the right context. No consideration of edge cases, error handling, or system failures.
The Technology Stack
Lists of frameworks, libraries, and deployment tools. No explanation of why these choices make sense for this specific business context.
The Black Box
"The agent will use advanced reasoning to solve customer problems." No detail about what that actually means in practice or how it will be implemented.
These designs fail because they treat the AI agent as an isolated system rather than a component in a complex enterprise ecosystem. They focus on what the agent will do, not how it will do it reliably, safely, and ethically at scale. They’re also too light on the explaining of the solution - too much technical definition, and not enough “why” added.
The Enterprise Agent Reference Architecture Reality
Before you write a single line of your solution design, you need to understand what you're actually building.
An enterprise AI agent isn't just a chatbot with access to APIs. It's a distributed system with multiple layers of orchestration, integration, operational oversight, and governance.
Below is my Enterprise Agent Reference Architecture diagram. I use this as my mental model of agent design to keep me on track.
Notice how the "Agent Core" is just one component among many. The entry points, orchestration layer, guardrails, enterprise integrations, and operational services are what make it work in practice.
Your solution design needs to address every layer of this architecture (and probably more in your organisation), plus the supporting systems that keep it running safely and effectively.
Building Your Solution Design: Layer by Layer
Entry Points & Integration Layer: Map Every Touch Point
This is where you demonstrate you understand how the real world works. List every way users or systems will interact with your agent:
User interactions: Slack bot, web form, mobile app, email integration
System triggers: File uploads, API webhooks, scheduled processes
Integration points: CRM systems, knowledge bases, escalation workflows
For each entry point, specify:
Authentication and authorization requirements
Data format and validation rules
Error handling and fallback procedures
Rate limiting and access controls
Don't just list the integration points, explain how they work together to create a seamless user experience.
User Experience & Interaction Design: Plan for Human Reality
Most technical designs skip this entirely, assuming UX is someone else's problem. In enterprise agent systems, interaction design is architecture. Poor UX leads to user workarounds that break your carefully planned integrations.
Define the interaction principles that guide every user touchpoint:
Agent persona consistency. How does the agent present itself across different channels? What's its communication style and tone?
Ambiguity handling. What happens when users ask unclear questions or request impossible actions?
Context preservation. How does the agent maintain conversation context across interruptions and channel switches?
Feedback mechanisms. How do users report problems or suggest improvements?
Escalation transparency. How does the agent communicate when it's handing off to humans?
Include specific examples of challenging interactions and how your design handles them. "When a user asks 'Can you fix my account?', the agent responds with 'I'd be happy to help with your account. To get started, could you tell me what specific issue you're experiencing?' rather than making assumptions about the problem."
Orchestration Framework: Design for Coordination
The orchestration layer is what separates enterprise agents from chatbot demos. This is where you define how multiple agents or agent instances coordinate, how work gets distributed, and how the system manages complexity.
Key decisions to document:
Agent routing. How does the system decide which agent handles which request?
State management. How do you maintain context across multiple interactions?
Queue management. What happens when demand exceeds capacity?
Service discovery. How do agents find and communicate with each other?
Include specific examples: "When a customer query comes in through Slack, the routing logic checks the user's account tier and query type, then dispatches to either the billing specialist agent or the technical support agent."
Core Agent Capabilities: Define What It Actually Does
This is where you get specific about the agent's behaviour. But frame it in terms of business capabilities, not technical features.
Structure this section around agent personas:
Customer Service Agent. Handles tier 1 support queries, escalates complex issues
Research Assistant. Synthesises information from multiple sources, creates summaries
Compliance Reviewer. Validates documents against policy, flags potential issues
For each persona, specify:
Reasoning approach. How does it make decisions? (ReAct, Chain of Thought, etc.)
Memory architecture. What does it remember and for how long?
Tool access. Which APIs, databases, and systems can it interact with?
Escalation triggers. When does it hand off to humans?
Include concrete examples of interactions, not abstract descriptions of capabilities.
Data Management & Lifecycle: Plan Your Information Architecture
Most designs treat data as an afterthought. In enterprise agent systems, data architecture is fundamental. Your agent is only as good as the information it can access and the quality of that information.
Address the complete data lifecycle:
Data sourcing. Where does the agent get its information? Internal documents, APIs, real-time feeds, historical records?
Data ingestion. How is information processed and prepared for agent use? What cleaning, validation, and enrichment steps are required?
Knowledge management. How do you structure information for retrieval? Vector databases, traditional search, hybrid approaches?
Data quality. What processes ensure information accuracy? How do you handle conflicting sources or outdated information?
Privacy and compliance. How do you handle PII, meet GDPR requirements, and manage data retention policies?
Context preservation. What information does the agent retain between conversations? How long is it stored? How is it protected?
Include specific examples: "Customer interaction history is stored for 90 days in encrypted form, with PII automatically redacted after 30 days. The agent can reference previous conversations but cannot access financial details without explicit user consent."
Model Lifecycle Management: Keep Your AI Current
The models powering your agent aren't static. They need updates, monitoring, and occasional replacement. Plan for this from the beginning.
Document your approach to the following.
Model selection: What criteria guide your choice between GPT-4, Claude, open-source alternatives? Size vs. capability trade-offs?
Version management: How do you test and deploy model updates without disrupting service?
Performance monitoring: What metrics indicate model drift or degradation? Response quality, accuracy, user satisfaction?
Continuous improvement: How do you fine-tune or retrain models based on real usage data?
Fallback strategies: What happens when your primary model is unavailable? How do you maintain service during model transitions?
Include specific monitoring thresholds: "Alert when response quality scores drop below 4.2/5 for two consecutive days, or when 'I don't understand' responses exceed 15% of total interactions."
Testing Strategy & Quality Assurance: Plan for Rigorous Validation
Enterprise agent systems require testing approaches that go beyond traditional software QA. You're testing not just functionality, but reasoning, safety, and user experience. The complexity of AI systems means your testing strategy must address multiple dimensions simultaneously, each with its own challenges and success criteria.
Your functional testing foundation should encompass unit tests for individual components, integration tests for system interactions, and comprehensive end-to-end tests for complete user workflows. But functional testing alone isn't sufficient—you need to validate that your agent can handle the performance demands of enterprise environments.
Security testing takes on particular importance with AI agents, as they present unique attack vectors. Your testing strategy must include traditional penetration testing for system vulnerabilities, but also adversarial testing specifically designed to detect prompt injection attempts and validate that access controls work correctly under pressure.
Safety testing addresses the AI-specific risks that traditional software doesn't face. You need to validate guardrail effectiveness under various conditions, conduct systematic bias detection across different user groups and scenarios, and ensure inappropriate content filtering works reliably.
User acceptance testing for AI agents goes beyond typical UAT scenarios. You're testing real user interactions, edge case handling, and escalation workflows, but you're also validating that the agent's responses feel natural and helpful to actual users.
Finally, your testing strategy must include continuous testing approaches that maintain quality over time. Automated regression testing ensures that updates don't break existing functionality, production monitoring catches issues in real-time, and A/B testing enables continuous improvement based on actual usage patterns.
Guardrail Implementation: Plan for What Goes Wrong
This section often gets relegated to an appendix, but it should be front and centre. Guardrails aren't just about preventing bad outputs—they're about maintaining system reliability and user trust.
The basic four categories of guardrails to address by default are:
Input validation: How do you handle malformed requests, injection attempts, and out-of-scope queries?
Processing controls: How do you prevent infinite loops, resource exhaustion, and model hallucinations?
Output filtering: How do you ensure responses meet quality standards, compliance requirements, and brand guidelines?
Escalation triggers: What conditions automatically involve human oversight?
For each guardrail, specify the detection mechanism, the response action, and the monitoring approach.
Security Architecture: Design for Enterprise Threats
AI agent systems introduce security challenges that most enterprises haven't encountered before. You're not just protecting traditional applications—you're securing systems that use reasoning, access multiple external services, and often integrate with workflow automation tools that bypass traditional security controls.
The tools powering modern agent systems create new attack surfaces that security teams rarely consider. Platforms like n8n enable agents to connect with hundreds of external services through automated workflows, often using API keys and webhooks that exist outside your standard IAM controls.
LangChain applications can dynamically load and execute code, access vector databases, and make API calls based on user inputs in ways that traditional security scanning tools don't understand.
The Model Context Protocol (MCP) allows agents to access desktop applications and local resources, creating potential pathways for data exfiltration that don't follow typical network security patterns.
Your security architecture needs to address both traditional threats and these AI-specific risks.
Enterprise Integration: Connect to Everything That Matters
This section demonstrates you understand that the agent doesn't exist in isolation. Map out every system connection with specific implementation details.
For each integration, document:
Connection method: REST API, database connection, message queue
Authentication approach: Service accounts, OAuth, API keys
Data synchronisation: Real-time, batch, event-driven
Error handling: Retry logic, circuit breakers, fallback procedures
Permission model: What data can the agent access and modify?
Include specific examples: "The agent connects to Salesforce via REST API using a dedicated service account with read-only access to case data and contact information. Connection failures trigger a 5-minute retry cycle before escalating to human operators."
Scalability & Performance Design: Plan for Growth
Enterprise systems need to handle increasing demand without degrading performance. Define your scalability architecture upfront.
Specify your performance requirements:
Throughput targets: How many concurrent conversations? Requests per second?
Latency requirements: Maximum response times for different interaction types?
Availability targets: Uptime requirements? Disaster recovery objectives?
Scaling strategies: Horizontal scaling for agent instances? Auto-scaling triggers? Load balancing approaches?
Resource management: Memory usage patterns? CPU requirements? Storage growth projections?
Capacity planning: How do you forecast and provision for growth?
Include specific metrics: "The system must handle 1,000 concurrent conversations with sub-3-second response times, automatically scaling agent instances when queue depth exceeds 50 pending requests."
Operational Infrastructure: Plan for Production Reality
Building the agent is just the beginning. Production operations require continuous monitoring, cost management, and maintenance that most teams underestimate. Your operational infrastructure needs to handle the unique challenges of AI systems—unpredictable token costs, model performance drift, and complex error states that traditional monitoring tools don't capture.
Include specific metrics and thresholds: "Alert when token costs exceed £500 per day or when response times exceed 30 seconds for 5 consecutive requests."
Ethical AI & Bias Mitigation: Design for Responsible Operation
Enterprise AI systems carry social responsibilities that extend beyond technical functionality. Your ethical framework isn't just about compliance—it's about building trust with users and stakeholders while avoiding the reputational risks of biased or unfair AI behaviour.
Include specific examples: "The agent undergoes monthly bias testing across demographic categories, with results reviewed by the Ethics Committee. Any bias detection scores above 0.3 trigger immediate review and remediation."
Implementation Roadmap: Sequence for Success
Success comes from delivering value incrementally while building toward a comprehensive solution. Start with a minimal viable agent that proves core concepts, then systematically add capabilities that increase business impact.
Structure your phases around business outcomes:
Phase 1 - Foundation: Core agent with basic routing and escalation capabilities
Phase 2 - Integration: CRM connectivity and structured human handoff workflows
Phase 3 - Intelligence: Advanced reasoning capabilities and multi-agent coordination
Phase 4 - Optimisation: Performance tuning, advanced analytics, and continuous improvement
For each phase, define specific deliverables, measurable success criteria, and required resources. This approach reduces risk while demonstrating continuous value delivery.
Governance Model: Define Ongoing Ownership
Long-term success depends on clear answers to fundamental questions: Who owns the agent? How are changes managed? What defines success? Your governance model should establish accountability and decision-making processes before deployment.
Define specific roles and responsibilities rather than abstract principles. Clear ownership prevents the diffusion of responsibility that causes many AI projects to drift or fail after initial deployment.
The Documents That Actually Guide Implementation
The solution designs that lead to successful implementations share common characteristics.
They're implementation-ready. Every major technical decision is documented with sufficient detail for developers to begin work.
They address enterprise concerns. Integration, security, governance, and operations are treated as first-class design considerations.
They include realistic constraints. Technical limitations, resource constraints, and timeline pressures are acknowledged and planned for.
They define success clearly. Specific metrics, measurement approaches, and acceptance criteria are established upfront.
They plan for evolution. The design acknowledges that requirements will change and the system needs to adapt.
They consider human impact. User experience, ethical implications, and social responsibilities are integrated into technical decisions.
Making Your Design Bulletproof
When you present your solution design, you're not just describing a technical architecture, you're demonstrating that you can think systemically about enterprise AI. You're showing that you understand the difference between a demo and a production system.
Your solution design should answer every question a skeptical enterprise architect might ask. How does it integrate? How does it scale? What happens when it breaks? Who maintains it? How do we know it's working? How do we ensure it's fair and safe?
When you can answer those questions before they're asked, you've moved from tool-user to solution architect. That's the difference between building AI projects and building AI systems.
Until the next one,
Chris