Intent Engineering is the discipline of aligning AI systems with business outcomes — not just generating responses, but achieving objectives. We help enterprises bridge the gap.
Optimizing how you phrase instructions to AI. Better wording, better templates, better outputs. Necessary, but limited to single interactions.
Optimizing what information you provide. RAG, retrieval pipelines, memory management. Better context, better reasoning continuity.
Optimizing what must be achieved. Defining objectives, success criteria, constraints, and stop rules so AI agents deliver business outcomes — not just responses.
Most enterprise AI implementations fail not because the models aren't capable, but because nobody defined what success actually looks like.
An AI agent can produce well-structured code, retrieve accurate data, and maintain multi-turn reasoning — yet still fail to deliver the business outcome it was built for.
Intent Engineering introduces a structural layer between your business objectives and your AI systems: objectives, constraints, autonomy boundaries, health metrics, and stop rules. It's the difference between telling an agent what to generate and telling it what to accomplish.
We evaluate your existing AI agent workflows and identify where intent is underspecified — where agents are executing tasks but missing business objectives.
We design the intent layer for your AI workflows. Objectives, success criteria, constraints, autonomy boundaries, and stop rules — structured and documented.
We embed with your team and build it. Migrate from prompt-driven to intent-driven agent workflows. Hands-on engineering with production-grade results.
Half-day or full-day intensive for engineering leadership on adopting intent engineering practices across your AI organization.
The progression from prompts to context to intent — and why the third wave changes everything for enterprise AI.
Real-world lessons from running AI agents against production workloads at scale — what worked, what broke, and why intent was the fix.
You upgraded the model. You tuned the context window. Your agents still aren't delivering. Here's the layer you're missing.
A structured approach to specifying what AI agents must achieve, how success is measured, and when to stop.
The gap between "it generated code" and "it solved my problem" is intent. Here's how to close it.
Why CTOs and engineering leaders — not just individual contributors — need to own the intent layer.
Intent Engineering isn't an academic theory I'm writing about from the sidelines. It's a discipline I've developed through years of running AI agents against real production workloads.
I manage a portfolio of 250+ production websites, migrating legacy systems to modern stacks using AI-driven workflows. I automate infrastructure at scale with PowerShell and Cloudflare. I ship code daily with Claude Code. I've seen firsthand what happens when AI agents are technically excellent but strategically misaligned.
That gap — between what agents can do and what the business needs done — is exactly what Intent Engineering solves.
RJL Software brings decades of hands-on engineering experience to every engagement. We're not consultants who draw frameworks on whiteboards. We're practitioners who build, deploy, and measure.
Whether you need an audit of existing workflows, architecture for new ones, or hands-on implementation — we'll help you get there.