Agents that know your company, not the internet.
A generic AI assistant knows the internet. It doesn't know how your team prices an exception, who gets escalated to, or what never gets said to a client. Decisionlore agents are grounded on your own verified memory, so they answer and act the way your company actually works.
Not a catalogue. A gap analysis.
It starts with a systematic audit of what your company's memory has already captured, with the access you've granted for that audit. That surfaces the real gaps: the questions staff keep asking, the process only one person actually knows, the judgment call nobody ever wrote down.
Agents get proposed from what the audit finds, not picked off a shelf of generic templates. If your memory doesn't hold enough to ground an agent well yet, that's the honest answer, and the gap becomes the next thing worth capturing.
Grounded, cited, and honest when it's stuck.
Answers cite what they used.
It says so when it doesn't know.
Access is what your company granted.
Nothing starts on autopilot.
Every agent climbs the same three rungs, in order. Each promotion is your company's decision, made with evidence of what the agent actually got right, not ours to make for you.
Watch
A person still does the work.
The agent observes and suggests. It doesn't touch the output, and it doesn't act. This is where every agent starts, no exceptions.
Draft
A person approves every output.
The agent prepares the work: a reply, a summary, a first pass. Nothing goes out until a person on your team reads it and approves it.
Autopilot
The agent acts, on a narrow, proven task.
Only for the specific task it has demonstrated it gets right, and only after your company decides, with evidence in hand, that it's ready.
Your key governs the agents too.
Agents only work with the access your company has granted, and whatever they learn along the way stays your company's, sealed the same way every knowledge card is. There is no separate door for agents to walk through, and no self-teaching mysticism either: an agent improves because the memory it draws from grows, and because your people keep verifying what's right. Every improvement traces back to a real card or a real correction.
About the agents layer.
Can an agent access things nobody has verified?
No. Agents answer only from knowledge cards a person at your company has reviewed and verified, and only with the access your company has granted. Unverified cards, marked captured, aren't in an agent's reach.
What happens when the agent doesn't know something?
It says so and stops, rather than guessing. That's the same honest behavior the memory layer follows when a document doesn't cover a question, carried through into the agent.
Can an agent go straight to acting on its own?
No. Every agent starts in watch, then draft, and only reaches autopilot for the narrow tasks it has proven itself on, one promotion at a time, decided by your company with evidence behind each step.
Does the AI teach itself over time?
No. It improves because the memory it draws from grows and because your people verify what's right. Every improvement traces back to a real card or a real correction, not to something learning unsupervised in the background.
See where the memory comes from.
Agents are only as good as what they're grounded on. Start with how that memory gets captured, sealed, and verified.