Multi-Agent Systems

Put multiple AI agents to work with your team

Acadamio helps you set up and coordinate AI agents to carry out practical tasks, with clear instructions, progress tracking and human approval.

From choosing a first use case to configuring a multi-agent system, we help you build an organisation that fits your tools and the way you work.

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What Acadamio sets up

Start with a clearly scoped pilot: a useful task, identifiable deliverables and approval criteria agreed before configuration.

A use case: the tasks to delegate, the scope and the expected results.
Roles: specialist agents and a supervisor agent responsible for coordination.
Instructions: context, approved sources, working guidelines and quality criteria.
Tools and access: the resources needed, subject to technical capabilities and available permissions.
Coordination: task allocation, context sharing and consolidation of results.
Human approval: review points and actions that require your agreement.

Diagram of a marketing multi-agent system with a coordinator and specialised agents

Examples of multi-agent organisation

These are possible setups to adapt to your context. Implementation depends on the tools, data and access available.

Marketing: from research to content ready for review

Agents research useful information, suggest topics, draft content and adapt it to the chosen channels. A supervisor agent distributes tasks, gathers the results and flags points that need review.

SEO/AEO: from analysis to proposed improvements

Agents analyse search questions, review existing content, identify gaps and prepare briefs or suggestions for improvement. A supervisor agent consolidates the findings and priorities.

How the engagement works

Scoping

Choose the first use case, deliverables, relevant data and expected level of supervision.
Configuration

Configure the agents and their coordination, their instructions and the agreed access.
Pilot project

Run a first workflow within a defined scope and review the results together.
Adjustments

Refine instructions, agent handoffs and review points based on the findings.
Getting started

Document how the setup works and help you assign tasks, track progress and approve deliverables.

What you receive

An agent organisation configured for the agreed scope.
A tested first workflow, with its results reviewed and limitations identified.
Documented instructions covering roles, context, coordination and approvals.
Guidance to help you track the work and adjust instructions.

Deliverables and support terms are agreed together before the pilot begins.

Frequently asked questions

What are the prerequisites?

A concrete use case, someone familiar with the work and the required data. Scoping checks the quality of sources, access and the AI tools needed.

Which tools can we use?

A multi-agent system organises the work of agents, which use the AI tools and resources configured in their environment. Connections to your current tools are assessed individually before being included in the pilot.

How much autonomy should agents have?

That depends on the task, risks and permissions. The pilot starts with an explicit scope and limits; sensitive actions and publication require human approval.

Who supervises the work?

The supervisor agent coordinates other agents and gathers their results. A designated person remains responsible for approving deliverables and actions; agent coordination does not replace that oversight.

What does it cost?

The cost of the engagement depends on the scope agreed together. Additional costs may come from AI tools, models and hosting, to be checked against the chosen configuration.

How is maintenance handled?

Instructions, access and tools can change. Follow-up and adjustment needs are discussed during scoping; no maintenance package is included by default.

Start with a concrete task

Which tasks would you like to delegate? Share your current tools, expected deliverables and desired level of supervision so we can define a first pilot.