WORK · AUTHORITY · OUTCOMES

Put AI agents to work.

Give agents real work, the tools and context they need, and clear boundaries for what they can do. Agentify keeps the work, decisions and outcomes together so agents can act without becoming a black box.

See Agentify at work → How it works

THE PROBLEM

An agent saying “done” is not the same as work being done.

A model can tell you it finished the job. Real work leaves state in the systems that matter: the pull request, CI result, approval, handoff or resulting record.

The work—not the transcript—is the source of truth.

THE AGENTIFY MODEL

The work—not the transcript—is the source of truth.

Work persists outside a model session. Capabilities stay bounded. Human decisions stay explicit. The execution layer can help perform the work without becoming the authority for what happened.

HOW IT WORKS

Agents should outlive a model session.

The work, context, capabilities and human decisions around an agent belong in the application—not inside one provider’s conversation.

Explore the platform →

APPLICATION-OWNED Agentify Work · execution · evidence

01Work statePersists beyond a call 02CapabilitiesOnly needed actions 03KnowledgeApproved context 04ExecutionModel/runtime is not authority 05ChannelsWhere work enters and leaves 06Human controlReview, approve, take over

Work state — Keep the authoritative work outside a model transcript.

Capabilities — Give agents only the actions and resource boundaries they need.

Knowledge — Ground work in selected, approved context.

Execution — Use models and runtimes to perform work without making them the system of record.

Channels — Bring work in and project results where people already operate.

Human control — Review, approve, hand off or take over when consequence requires it.

IMPLEMENTED WORKFLOWS

One platform. Very different jobs.

01 Software Engineer 02 AI Front Desk 03 Microsoft 365 Procurement

ONE FOUNDATION Application-owned state + control

Execution Broad, isolated Restricted actions Native + isolated

Context Repository + task Selected knowledge Versioned SharePoint evidence

Continuation Run + session Conversation + intake Teams request + workflow

Human boundary Merge judgment Handoff Manager approval

Software Engineer

Give a coding agent real engineering work. Agentify tracks it through isolated execution, CI, review and the final human merge decision—even when the coding session ends.

See the full lifecycle →

AI Front Desk

Selected knowledge, limited actions, structured intake and an explicit human handoff. The public agent cannot turn conversation into authority it was never given.

Microsoft 365 Procurement

A signed Teams request becomes durable work. Specialist agents review selected SharePoint evidence, an authorized manager decides in Agentify, and an approved Power Automate action is read back before Teams receives completion.

Review the product proof →

CONNECTED WORK

From a human request to a verified outcome.

The Calgary procurement workflow exercises the product as one system—not as a collection of provider demos.

01
Identity and intake
A workforce user signs in with Microsoft Entra ID. A tenant-bound, signed Teams activity creates one durable work item.
02
Evidence and specialist work
Multiple persistent agents inspect exact SharePoint source versions, work concurrently and use both native and isolated execution strategies.
03
Human decision
An approval owned by Agentify pauses the workflow until the exact authorized manager approves or rejects it.
04
Action and readback
Power Automate creates the approved record once, SharePoint stores the decision artifact, and provider readback—not model narration—closes the loop.