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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 →
Agentify keeps work, capabilities, knowledge, execution, channels and human control inside an application-owned system of record.
APPLICATION-OWNED Agentify Work · execution · evidence

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.

Choose an Agentify workflow
One Agentify foundation reconfigures its capability matrix for the selected implemented workflow.
ONE FOUNDATION Application-owned state + control
Execution Broad, isolated Restricted actions
Context Repository + task Selected knowledge
Continuation Run + session Conversation + intake
Human boundary Merge judgment Handoff

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.

REAL PRODUCT STATES

AI work you can prove.

Know what the agent did. Know what happened in the systems around it. Keep the human decision where consequence requires it.

Review the evidence boundary →

Software Engineer

Choose a Software Engineer product state
Agentify orchestrator overview showing four persisted software-engineering runs in active, CI fixing, review addressing and ready-to-merge states.
Agentify software-engineering run detail showing a persisted pull request, review round, agent conversation and tool-execution history.
Agentify software-engineering run paused at the ready-to-merge state after green CI and resolved review feedback, awaiting human judgment.

AI Front Desk

Choose an AI Front Desk product state
Published Agentify Front Desk configuration showing selected public knowledge, allowed topics, intake fields, handoff destination and a pending human review.
Agentify public Front Desk showing a grounded conversation, completed structured intake, persisted context and an explicit human-review escalation.

AGENTIFY BY AMIDSHIP

Govern the work, not just the prompt.

Give agents room to act while keeping authority, evidence and consequential decisions visible.

Explore enterprise use → Compare approaches

Agentify by Amidship.