Intent-Driven Engineering to Level 4 Maturity
- Mark Kendall
- 15 hours ago
- 3 min read
🧠
Intent-Driven Engineering to Level 4 Maturity
How to Build an AI-Operated Platform on AWS
🔷 Introduction
Most organizations today are experimenting with AI—chatbots, copilots, and automation scripts. But very few have moved beyond assistance into true operational capability.
This article explains how to evolve from using AI as a tool to building a system where AI operates your workflows.
We call this:
Level 4 Maturity — an AI-operated, intent-driven platform
This is not about a single tool. It’s about architecture, orchestration, and control.
🔷 What Is Level 4 Maturity?
Level 4 maturity means:
Business events trigger actions automatically
AI determines how to execute the work
Systems integrate and update without manual intervention
Failures are handled with retries and safeguards
Humans monitor—not operate
🔷 What Is Intent-Driven Engineering?
Intent-Driven Engineering is a way of building systems where:
You define what you want done (intent)
The platform decides how to do it
Instead of writing step-by-step logic everywhere, you define structured intent like:
intent:
type: "sync-location"
source: "nautobot"
target: "salesforce"
rules:
- validate data
- prevent duplicates
- retry on failure
The system takes it from there.
🔷 The Target Architecture (AWS Example)
Here’s what a Level 4 system looks like on AWS.
🟦 1. Intent Entry Layer (How Work Starts)
Tools:
API Gateway
EventBridge
Webhooks
What it does:
Receives events (new data, requests, changes)
Converts them into structured intent
👉 This replaces tickets and manual triggers
🟪 2. Orchestration Layer (The Brain)
Tools:
AWS Step Functions
AWS Bedrock
Custom orchestration service (Spring Boot / Python)
What it does:
Reads the intent
Decides what steps are needed
Chooses which AI or service to use
👉 This is where decisions happen
🟩 3. AI Agent Layer (Execution Intelligence)
Tools (fit-for-purpose):
Claude → reasoning, architecture, complex decisions
Codex-style models → precise code generation and refactoring
Bedrock models → AWS-native execution
What it does:
Generates code
Analyzes data
Makes decisions within guardrails
👉 Different models do different jobs
🟧 4. Execution Layer (Your Existing Systems)
Tools:
Spring Boot microservices
Kafka (with DLQ)
REST APIs
Salesforce / ServiceNow integrations
What it does:
Executes real business operations
Moves data between systems
Applies business rules
👉 This is where value is delivered
🟥 5. Governance Layer (Control and Safety)
Tools:
CloudWatch / OpenTelemetry
IAM policies
Audit logging
What it does:
Tracks everything
Enforces security
Ensures compliance
👉 This is what makes Level 4 safe for enterprise
🟨 6. Memory & State Layer (Learning System)
Tools:
DynamoDB / Aurora
Vector database (RAG)
What it does:
Stores past executions
Learns from outcomes
Improves decisions over time
👉 This turns automation into intelligence
🔷 How It Works (End-to-End Flow)
Event occurs (API call, webhook, system update)
Intent is created automatically
Orchestrator reads intent
AI agents plan and execute steps
Systems are updated (via APIs/microservices)
Results are validated
System logs outcome and learns
Only exceptions are escalated
🔷 Why This Matters
Without this model:
AI stays a tool
Humans stay in the loop
Scale is limited
With this model:
Workflows run continuously
Systems adapt faster
Teams focus on higher-value work
🔷 Key Takeaways
Level 4 is not a tool—it’s a system capability
AWS provides the foundation, not the full solution
Multiple AI models should be used together
Orchestration is more important than the model
Governance is required for enterprise adoption
🔷 Final Thought
Most organizations are asking:
“How do we use AI?”
The better question is:
“How do we build systems that AI can operate?”
That’s the shift from experimentation to true engineering maturity.
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