Roles / AI transformation leader
For AI transformation leaders
From prompt engineering to intent engineering
Your pilots work. Turning them into value at scale is the hard part – and it depends less on the model than on whether your agents can read what your organisation actually wants. Gluu makes your intent machine-readable, governed, and current.
Sound familiar?
Pilots are easy. Production is hard – and the gap isn’t the model.
Your pilots work, but they aren’t translating into value at scale.
Your agents know what your organisation knows – not what it actually wants.
Your real intent lives in heads and chat threads, where no agent can read it.
You’re measured on AI ROI the business can’t yet see.
What it’s costing you
An agent that succeeds at the wrong thing is the expensive kind
The Klarna cautionary tale is what happens when an agent is fast, measurable and quietly optimising for the wrong thing. Without a governed source of intent, that risk scales with every agent you add.
The pilot-to-production gap
The blocker between a working pilot and scaled value is usually the organisation around the agent, not the agent itself.
Optimising the wrong thing
An agent told what to know but not what to want will hit its metric and miss the point – fast.
Intent drift
A long-running agent acting on guidance that has quietly gone stale keeps drifting further from what you meant.
Imagine instead
Agents that run on what your organisation actually wants
Your organisation’s intent made machine-readable, so agents optimise for what you truly want.
Pilots turning into scaled value because the foundation underneath them is solid.
Agents running on a governed way of working instead of guessing from fragments.
AI capability you can show process by process – not just activity.
How Gluu does this for AI transformation leaders
Make intent explicit, keep it current, and let agents act on it safely
01 · Capture
Make intent machine-readable
Context tells an agent what to know; intent tells it what to want. Gluu captures how your organisation actually works, at the point of work, so agents optimise for the outcome you meant.
“Intent engineering” – term credited to Nate B. Jones.
02 · Govern
Keep intent current
Telemetry closes the loop between intent and reality, so guidance updates as your processes change and long-running agents don’t drift onto stale instructions.
A self-correcting intent layer, not a static prompt library.
03 · Follow
From assist to operate, safely
When you’re ready for agents to act and not just answer, MCP exposes your governed process graph as callable tools – with humans at the boundaries that matter.
Assist today, operate tomorrow – on a foundation you govern.
The shift
Context tells an agent what to know. Intent tells it what to want.
Prompt engineering got the answer out of the model. Intent engineering makes what your organisation wants machine-readable and governed. This is a forward promise – governed AI is where Gluu is heading, built on the adoption you can prove today.
Questions AI leaders ask
Intent engineering, answered
Where prompt engineering gets a good answer out of a model, intent engineering makes what your organisation actually wants machine-readable, so agents optimise for the outcome you meant – not just the information they were given. The term is credited to Nate B. Jones.
Usually the blocker isn’t the model but the organisation around it: agents can’t reach a governed, current view of how work is meant to be done, so what works in a demo breaks at scale.
Intent lives at the point of work. Gluu captures how your organisation actually operates and keeps it governed, so an agent runs on a shared way of working instead of guessing from scattered fragments.
Give it a governed source of intent, not just data. An agent told what to want, with an owner and a way to correct it, is far less likely to hit a metric while missing the point – the risk behind widely-cited cautionary cases like Klarna.
Telemetry closes the loop between intent and reality, so guidance updates as the work changes and long-running agents don’t keep acting on stale instructions.
The processes they act on need to be explicit, current, owned and governed. Then MCP can expose that process graph as callable tools, with humans kept at the boundaries that matter.
Give your agents something to trust
Book a demo and we’ll show how a governed process becomes the intent layer your agents run on – and how the pilot-to-production gap closes.
From EUR 24 / year – less than a drawing tool.
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