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Gezora.ai

Design workflows an AI agent can safely run

Gezora designs agentic workflows: what an agent decides on its own, what it hands to a person, and what it must never do, then builds and runs the result.

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The parts of an agentic workflow

  • Autonomy boundaries
  • Approval points
  • Blocked actions
  • Multi step orchestration
  • Shared context
  • Context handoff

What we build inside an agentic workflow

  • Decision boundaries for every workflow
  • Orchestration across agents and systems
  • Escalation to a named owner
  • Logging and run monitoring

Where autonomy stops and a person starts

  • Autonomy with limits

    The agent acts on its own only inside boundaries your team agreed before anything was built.

  • Human approval where needed

    Sensitive steps stop and wait for a named owner rather than being decided by the agent.

  • Work that finishes

    Multi step work runs across agents and systems, with retry and recovery when a step fails.

  • A complete audit trail

    Every decision is logged with its reasoning, so you can review afterwards what the agent did.

The parts of an agentic workflow

  • Autonomy boundaries
  • Approval points
  • Blocked actions
  • Multi step orchestration
  • Shared context
  • Context handoff
  • Retry and recovery
  • Decision logging
  • Run replay
  • Escalation paths
  • Run monitoring
  • Boundary review

What we build inside an agentic workflow

Each workflow is designed around the decisions it contains, then built, connected to your systems, and watched once it runs.

  • Decision boundaries for every workflow

    Before anything is built, we write down what the agent may decide alone, which steps need a human approval, and which actions are blocked outright. Autonomy becomes a deliberate choice rather than something that emerges once the workflow is running.

  • Orchestration across agents and systems

    Multi step work is coordinated across several agents and the systems they touch, with shared state so each step knows what the one before it did. Retry and recovery handle the failures that come with messy business data.

  • Escalation to a named owner

    Anything that falls outside the agreed boundary is escalated to a named owner rather than guessed at. The person picking it up sees the context the agent had, so the handoff does not restart the work.

  • Logging and run monitoring

    Every decision an agent makes is written down with the reasoning behind it, so a review after the fact has a complete trail. Run monitoring shows what is running, what is waiting, and what stopped.

How we run the engagement

Every step happens with your team, and nothing is built before the boundaries are agreed.

  1. 01

    Step 1, Workflow review

    We walk the workflow with you and mark every point where a decision is actually made.

  2. 02

    Step 2, Boundary design

    We agree what the agent decides alone, what needs approval, and what stays blocked.

  3. 03

    Step 3, Build to the boundary

    Orchestration, shared context, and retry handling are built around the boundaries already agreed.

  4. 04

    Step 4, Trial run

    The workflow runs against real cases while logging and escalation are checked step by step.

  5. 05

    Step 5, Live with monitoring

    It goes live with run monitoring, decision logs, and a named owner for escalations.

The workflows worth handing to an agent

The workflows worth handing to an agent are the ones where the same decision gets made over and over and someone still has to sit in the middle of it.

  • Operations teams with long approval queues

    Work waits because every step needs someone to look at it, including the steps nobody ever disputes. The agent takes the routine decisions inside a boundary your team agreed, and stops at the ones a named owner has to approve.

  • Companies whose work spans several systems

    A single piece of work moves between systems today, and context is lost at every handover. Orchestration keeps shared state across the steps, so each one knows what the step before it did.

  • Teams whose rule based automation keeps breaking

    Fixed rules cover the clean cases and stall on everything else, so the exceptions come back to the team anyway. An agent chooses its next step inside the boundary you set, with retry and recovery for the messy cases.

  • Leaders accountable for what automation decides

    Signing off on automation is hard when nobody can say afterwards why it did what it did. Every decision is logged with the reasoning behind it, and a run can be replayed step by step during review.

What stays with your team

An engagement leaves your team with the boundary decisions written down and the workflow running against them.

  • A written boundary map for the workflow
  • An approval point list with named owners
  • A written register of blocked actions
  • The orchestrated workflow running across your systems
  • Decision logs with the reasoning behind each step
  • A run monitoring view with a named escalation owner

The boundary decisions are written down before the build, and you keep that writing along with the workflow running against it.

FAQ about Agentic AI Workflows

Straight answers on scope, timelines, and what running Agentic AI Workflows asks of your team.

Other work that pairs with this

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Stop paying people to do what an agent can

Tell us what you want to automate. We will map the workflow, deploy the right agents, and train your team to run them.

  • Every agent is trained on your own workflows, never a generic template
  • Most deployments are live within two to four weeks
  • SOC 2 compliant, with a complete audit trail on every deployment
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