Operate the workflow, keep the decision rights
Managed AI agent service for owner-led B2B firms
A managed AI agent service is an operating engagement in which a provider assesses, builds, runs, and maintains a defined AI-supported workflow. AI Jungle starts with the work your firm needs done, not an agent catalog. The written scope names the input received, the approved sources, what the agent may prepare, the human who decides, and the conditions that stop the workflow. Confirmed corrections and open exceptions remain in an operating record. The team receives the workflow map, source boundary, approval policy, maintenance duties, and handoff record described in the agreement.
This is for owner-led B2B firms that want a provider to operate a workflow while the firm keeps its decision rights. It is not a self-serve agent platform, and it does not remove the need for a named human reviewer.
Published August 13, 2026. Updated October 11, 2026.
What is delivered, who decides, and what the team can resume
The matrix makes the service inspectable before a buying conversation. Each row connects an artifact to evidence, responsibility, validation, a stop condition, and the state left for the team. The final contents remain subject to the written scope and agreement.
| Deliverable | Evidence | Human owner | Validation | Stop | Handoff state |
|---|---|---|---|---|---|
| Assessment and prioritized workflow map | Named input, expected output, approved source list, decision criteria, and open questions | Firm sponsor and the person responsible for reviewing the work | The firm confirms the selected workflow, its sources, and the expected output before a build decision | Stop or defer when the output, source boundary, reviewer, or approval point cannot be named | A readable map of the candidate workflow and the reason to build, simplify, defer, or stop |
| Build boundary and approval policy | Written permissions for what the agent may read, prepare, change, or send, plus the evidence shown to the reviewer | AI Jungle owns implementation inside the scope; the firm names the approval owner | The approval owner reviews prepared work against accepted examples and stated criteria | The workflow stops when an input, permission, source, or decision falls outside the boundary | Current boundary, approval point, exception route, and accepted examples are recorded |
| Run and maintenance record | Confirmed corrections, open exceptions, workflow changes, and agreed outcome evidence | AI Jungle owns the operating and maintenance duties named in scope; the firm owns its business decisions | A correction enters the method only after the firm confirms it; unresolved cases remain exceptions | Pause when required evidence is missing or a case needs a decision that the workflow does not hold | The team can inspect the current method, pending exceptions, and maintenance responsibilities |
| Team training and handoff record | Workflow map, source and access boundary, approval policy, exception route, training material, and maintenance duties included in scope | AI Jungle prepares the handoff; the firm names the people who receive and operate it | The receiving team uses the same boundary and record to review prepared work and route an exception | Do not treat the handoff as complete when access, documentation, or ownership remains unnamed | The agreement identifies the components, accounts, access, export material, and duties the team receives |
Managed service vs managed platform
“Managed” describes two different buying models.
| Question | Managed AI agent service | Managed agent platform |
|---|---|---|
| What is bought? | Operation of a defined firm workflow, with delivery and maintenance duties written into the scope | Software infrastructure and the controls the vendor states for building and managing agents |
| Who designs and runs the firm workflow? | The service operator carries the duties named in scope; the firm retains named approvals | The customer must name the people who configure, evaluate, operate, and maintain its workflow |
| What should be inspected? | Workflow boundary, approved sources, approval policy, exceptions, corrections, maintenance, training, and handoff | Product controls, support terms, configuration, evaluations, access, and export options stated by the vendor |
| When does it fit? | When external operation and maintenance are part of the need | When the firm has a capable owner for configuration and ongoing operation |
This is a category distinction, not a platform ranking. A platform may be the better route when your team wants to build and operate the workflow. A managed service fits when the provider’s continuing operating responsibility is part of what you need. Compare the written proposal and agreement, not the word “managed.”
What can you inspect in public before you buy?
You should not need an account or a calculator to understand the operating model. The public dossier for this offer consists of:
- The service scope on this page: the workflow map, operating boundary, approval policy, operating record, maintenance, training, and handoff categories that the offer names.
- The decision path: the agent prepares work from approved sources; the named reviewer receives the source, prepared output, and open questions; the workflow stops outside its written boundary.
- The responsibility split: AI Jungle performs the implementation and operating duties included in scope. Your firm names the people who own business criteria and approvals.
- The handoff definition: the scope identifies which components, accounts, access, documentation, export material, and maintenance duties are included. Items not named should not be assumed.
- The operating method: read how sources, approvals, exceptions, and maintenance are handled.
- Commercial terms: check the current pricing page. This page does not restate figures.
- Named authorship: the operator responsible for the claim is identified below.
These are public, inspectable commitments: named scope fields, the decision path, the responsibility split, the handoff definition, the method page, and current commercial terms. They let a buyer audit the operating model before a call. They are not a client case study, not third-party certification, and not a guarantee of results. Any worked example must stay labelled as an example unless a real test and its authorization are documented.
What should stay out of the first build?
Keep the first build small. Pick one workflow with a clear input and a clear output. Do not start with a stack of tools. Do not start with a promise to replace a person. Start with the work the firm already understands.
Write the source list before any model choice. Write the approval owner before any production send. Write the stop rule before the first live case. If those three lines are missing, the build is early.
A short checklist helps the sponsor decide:
- Name the workflow in one sentence.
- Name the output a reviewer can accept or reject.
- Name the sources the agent may read.
- Name the human who keeps decision rights.
- Name the event that stops the run and opens an exception.
If the firm cannot fill that list, pause. A pause is a valid outcome of the assessment. It protects budget and trust.
How do maintenance and training stay useful over time?
Maintenance is not a vague support hour pack. It is the duty to keep the workflow map, the boundary, and the exception route current when the firm changes a source or a rule. Training is not a slide deck alone. It is practice on the same record the reviewer will use on a real case.
When a correction is confirmed, it should enter the method the next operator can read. When a case stays open, it should stay visible as an exception. That is how the team resumes work after a holiday, a staff change, or an end of engagement.
The public method page explains the operating habits. The pricing page holds commercial terms. This page holds the service shape: what is delivered, who decides, and what the team can resume. Read all three before you approve a build in 2026.
Questions about a managed AI agent service
What does a managed AI agent service include?
AI Jungle’s service includes workflow assessment, a custom build around approved sources and existing tools, a written operating boundary, named human approvals, maintenance, exception review, team training, a handoff record, and agreed outcome tracking for the selected workflow. The signed scope determines the exact inclusions.
Does the agent make decisions for the firm?
No. The agent may prepare only the work allowed by the written boundary. A named person retains the firm’s sensitive, external, or otherwise reserved decisions. If a source, permission, input, or decision falls outside the boundary, the workflow stops and routes the exception.
How is this different from a managed AI agent platform?
A platform supplies software and stated controls for a customer team that builds and runs agents. This managed service assigns a provider operating duties around a defined firm workflow. The useful comparison is who configures, evaluates, maintains, approves, and receives the operating record.
What evidence should we request before approving a build?
Ask for the named workflow, expected output, approved sources, written permissions, review owner, validation criteria, stop conditions, exception route, maintenance duties, and handoff contents. If those fields cannot be made explicit, the build decision is premature.
What can our team continue without the service operator?
That answer must be written into the handoff scope. It should identify the workflow map, sources and access, approval policy, accepted examples, confirmed corrections, exception route, training material, components, export material, and maintenance duties the team receives. Anything agreement-specific should be marked as such before work starts.
Written by Loïc Guyon (Tileo), the operator who runs AI Jungle's own agent workforce.