Blog
Engineering insights on building AI agents that learn.
You Have an Agentic Mandate. Now What?
AI leaders are told to make the organisation agentic. Most get pilots, not an operating model. Start with an experience layer from your enterprise systems, then design the agent architecture that uses it.
Nobody Wrote Down the Order
Backfill before cutover. Check before you drop. The sequence that keeps you out of an incident lives in one engineer's memory and in no document.
Plausible Is Not Proven
Two approaches read equally well. One of them has already failed in your environment. Nothing in the text tells your agent which one.
You Already Wrote the Rule Down
The correction you gave your agent last week is sitting in AGENTS.md right now, and it will be ignored again today. A rules file was never an enforcement mechanism.
Agents Can Now Start With Experience
Hyperstruck agents no longer need to learn every basic lesson from your team the slow way. They can start with proven SDLC and security experience on day one.
Stopping Agents From Reporting False Success
The agent failure that actually costs you isn't the crash you can see. It's the confident 'done' on work that never happened, and you find out from the customer.
Innate Intelligence
An agent that learns is emptiest on the day someone first judges it. Ours don't show up empty, they arrive already wary of the mistakes everyone makes, then learn the ones only you make.
Run Your SDLC in Co-Pilot Mode: A Developer's Getting-Started Guide
A hands-on walkthrough for enterprise developers: wire Hyperstruck into your software development lifecycle in co-pilot mode, so reasoning sharpens every plan and learnings compound across discovery, design, build, and validation.
A Hunch Is Not a Rule
Once an agent starts learning, it will eventually act on a lucky guess like it's a law. I traced it back to one number trying to do two jobs.
Where Agents Lose the Plot
An agent can nail five steps and hand you a fragment of the last one. The last line it writes tells you what it was really built to do.
Sharing Learnings Without Leaking Secrets
When an agent's lesson is good enough to share, every value it touched needs a privacy call. Scan the bytes for secrets and you are guessing at something the bytes cannot answer.
Why we didn't build Hyperstruck on a graph
A graph is a great way to describe a process you already understand, and the wrong way to build an agent. The whole reason to reach for an agent is that you cannot specify the steps up front.
A Repeated Lesson Is Not a Duplicate
Agents learn the same lesson over and over. Deduplicating those copies throws away the best signal you have.
Agents Need to Forget
When agents learn from experience, a new failure mode appears: they don't know when to stop believing what they learned.
Labels Are a Liability
We built a taxonomy for agent learnings, with categories and routing logic in every consumer. Then we deleted all of it and got better results.
Most of What Your Agent Learns Is Garbage
Your agent extracts learnings from every task. But pull up the last fifty and read them. Half are just the tool's documentation with extra words.
Frontier Performance from an Open-Source Model
We beat GPT-4o on SWE-bench Verified using llama-3.3-70b at 12x lower cost per fix. The model isn't smarter. It's being used better.
What Are Learnings?
Every agent framework has memory. Almost none have learnings. The difference is why your agent makes the same mistake twice.
LLMs Can't Count to Five
When you ask an LLM to score quality on a scale, it secretly uses three buckets. The fix: stop asking models to judge and start asking them to extract evidence.
The God Model Trap
Several of our components were pinned to one provider's model. Then they changed it overnight. The fix wasn't finding a better model. It was building for the swap.
Your Agents Start From Zero Every Morning
A live demo showing why the next wave of AI isn't about faster answers. It's about compounding intelligence across your software development lifecycle.