AI agents and automation
Most organizations are done experimenting. The first Copilot licences are in use, teams are building things themselves, and expectations are rising.
Where projects actually get stuck
In most projects, technology is not the bottleneck. These four things are:
- Agreement on where automation will create measurable value
- A path from prototype to production
- Integration into the systems where work really happens
- Clear ownership after go-live
An agent that gets built in a week and forgotten in a month is a cost, not a saving.
Not everything needs an agent
Automation and agents solve different problems. Choosing the right tool is half the work.
Rules-based automation
Knowledge agents
Process and task agents
Custom and autonomous agents
From idea to production
Identify
Find the cases worth automating.
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Map processes, bottlenecks and repetitive work
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Estimate business value against effort
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Check data readiness and integration needs
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Decide whether a flow, an agent or a person is the right answer
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Prioritize a realistic roadmap
If you cannot explain the agent's job in one sentence, the scope is probably too broad.
Design
Define what the agent does, and what it does not do.
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Scope and success criteria
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Data sources, permissions and integrations
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Actions the agent is allowed to take
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Human-in-the-loop checkpoints
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Ownership, monitoring and compliance requirements
Most governance problems are design problems. They are much easier to solve before development starts than after deployment. AI governance and managed operations.
Build
A demo only needs to work once. Production needs to work every day.
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Copilot Studio agents
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Custom agents on Azure AI Foundry
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Dynamics 365 agents
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Power Automate and Logic Apps automations
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Integrations to ERP, CRM, SharePoint and line-of-business systems
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Evaluation, testing and security review before go-live
That is why we spend as much time on permissions, integrations, testing and monitoring as we do on prompts and agent logic. Everything we hand over has an owner, documentation and a defined operating model.
Deploy and adopt
Get the agent into people's hands.
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Publishing to Teams, Microsoft 365 Copilot, web or business applications
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User onboarding and role-based training
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Feedback loops from the first week onwards
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Usage and value measurement
Adoption matters more than technical success. If the people doing the work do not trust the output, the project fails regardless of how good the technology is.
Operate
Most AI projects do not fail on launch day.
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Monitoring and incident handling
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Platform and model updates
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Consumption and licence cost tracking
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Continuous improvement based on real usage
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Regular reviews with the business owner
They fail six months later, when source data changes, processes evolve, licences change or nobody notices that answer quality is gradually declining. Running agents is an operational responsibility, not a project task.
Agents we have already built
Sales offer agent
Knowledge assistant
Customer service agent
Order and supply chain agent
Maintenance and field service assistant
Finance and procurement automation
Built on the Microsoft platform you already have
- Microsoft 365 Copilot and Copilot Studio
- Azure AI Foundry
- Power Platform
- Dynamics 365: Business Central, Sales, Customer Service, Field Service
- Dataverse and Microsoft Fabric for data
- Entra ID, Purview and Defender for identity, data protection and security
We build on what you already license and already secure. No parallel AI stack to maintain, and no second set of permissions to keep in sync.