A discovery path into agentic work
Sit with us to map your first workflow, then see what shipped: ROI reporting you can build, house rules that stick, and cleaner facts agents can trust.
Most teams do not need another agent demo. They need a clear first step. We now run a facilitated discovery that walks you through agentifying one costly workflow, so the next ninety days have an owner, a shape, and metrics you can stand behind.
Sit with us and map the first workflow
Agentic Inception is a half-day working session. In the room you leave with a solution architecture, an agentic architecture for who owns what, a clear view of what your agents should actually be learning, and success metrics for the next leadership conversation.
Reply to this email or write hello@hyperstruck.com with subject "Agentic Inception discovery" and we will set it up.
Build the ROI board yourself
Spaces now have their own dashboards, plus a drag-to-build report surface. You can plot the numbers a pilot actually needs, for example:
- Time saved retrieving context from documentation and prior runs
- Employee hours (and dollars) saved by avoiding rework
- How often agents were correctly steered by existing experience
- Learnings applied in runs, versus runs that still cold-started
- Platform spend next to platform ROI
The point is a board a steering group can read without a custom BI project.
House rules your team states can stick
When someone on your team tells an agent a standing rule, Hyperstruck can keep that rule for later work, not only what it figures out by watching a task play out.
Example: a release lead tells the agent "never open a production change without the change ticket." That instruction can become a lasting standard the next release run already has, instead of living only in the chat where it was said.
Same fact, one record
Agents used to store near-duplicate copies of the same business fact, which meant conflicting answers about the same customer or invoice. Hyperstruck now keeps one record per fact, can recover facts that were already learned, and lets a reviewer undo a bad merge. Collections sees one payment-terms record for Customer X, not three that disagree.
See which lessons actually changed the work
A run can now name the lessons that applied, not just that "experience helped." That makes the learnings-applied chart honest, and gives a stakeholder something concrete to review.
Why closing the loop matters
Replit, OpenAI and Meta each published that agents fail on context, not capability. We have been building for that diagnosis for some time: not only richer context, but a closed loop where how the work turned out feeds back into what agents know, so behaviour actually changes on the next run. That feedback into judgment is still the part most stacks leave open. We wrote it up here: Replit, OpenAI and Meta Fixed Agent Context. Not One of Them Published a Number.