Microsoft AI Agents
Copilot Studio and Azure AI Foundry agents delivered with governance from day one. Discovery, pilot, governance and adoption from one Microsoft-specialist team. RapidLogic™ delivery.
Why it matters
A successful AI agent programme is a governance programme that happens to use agents as the delivery surface. The pattern that fails is the inverse: an impressive Copilot Studio demo approved for broader use before the agent identity is defined, before grounding scope is locked down, before tool and connector authorisation is reviewed, and before audit telemetry is hooked into the security operations stack. Procurement, audit and risk teams ask the same question in different language: which data can this agent see, which actions can this agent take, and how is that verifiable. UHS Logic delivers AI agents with the framework in place from day one.
An agent in production carries a workforce-level capability with a service-account-level identity. Treating it like a demo is how a sensitive document surfaces in an unrelated agent response. We do not deliver demos and call them programmes.
What we deliver
Each accelerator is a discrete offering with its own scope, deliverable and exit criteria. Pick the accelerators that match the engagement and we respond against those, not a generic "AI agent services" framing.
A prioritised, role-mapped backlog of agent use cases tied to measurable business outcomes.
A measurably outcome-bound custom agent grounded against your line-of-business systems, built on Copilot Studio.
A developer-built agent on Azure AI Foundry with retrieval-augmented grounding and a defensible evaluation harness.
Identity, data, tool and audit posture for AI agents documented before the first agent ships into production.
Change management and role-based training for an agent-first workflow, co-delivered with the UHS Logic training practice.
Governance framework
Layers sit underneath every UHS Logic AI agent engagement. Each layer is concrete: an identity configuration, a grounding policy, an authorisation boundary, an audit hook. None of them is a marketing claim.
Each agent runs under a dedicated Entra identity, not a shared service account. On-behalf-of (OBO) flows preserve the calling user's permissions. Privileged operations require a separate elevated identity, never the agent's own credentials.
Microsoft Purview sensitivity labels determine what the agent can read at grounding time. Out-of-scope data is documented per agent specification, not retrofitted after a sensitive document surfaces in an agent response.
Which actions can this agent take, against which systems, on whose behalf. Authorisation boundary defined in the agent specification and verified before the pilot proceeds. Read-only agents and write-capable agents follow different review tracks.
Microsoft Responsible AI principles applied per agent: fairness, reliability and safety, privacy and security, inclusiveness, transparency, accountability. Out-of-scope use cases are documented as such.
Every agent action logged: input, grounding sources retrieved, tools called, output returned. Telemetry integrated with Sentinel and Defender for security operations visibility. Visibility into what each agent is doing in your environment, by whom, for what purpose.
Agent versioning, staged rollouts, decommissioning posture. Agents are not one-shot demos; they are production workloads with a lifecycle. We define the lifecycle posture before the first agent ships.
Methodology
Three phases. Discovery gates pilot. Pilot gates scale. No phase is skipped because the next one is more interesting.
Use case discovery workshop, agent surface selection (Copilot Studio vs. Azure AI Foundry), grounding data scope, identity and authorisation model, Responsible AI considerations per agent. Outputs an agent specification and a go/no-go for pilot.
Single agent built against the approved specification. Adoption metrics defined. Governance posture verified in real use. Evaluation harness produces quality and safety scores. Gaps identified for resolution before broader agent rollout.
Multi-agent rollout against the validated framework. Lifecycle management posture handed over to your team. Training practice co-delivered for adoption. Hypercare period defined explicitly, not assumed.
RapidLogic™ is the implementation methodology of UHS Logic. It is applied across all Microsoft AI agent engagements.
Why UHS Logic
Microsoft Solutions Partner designations covering Data and AI and Digital and App Innovation. Designations carry through procurement and audit review, not a marketing line.
We do not deliver LangChain in production for enterprises or AWS Bedrock agents. Every consultant carries Microsoft as the core competency. AI agent delivery is grounded in current Copilot Studio and Azure AI Foundry practice, not retrofitted from a generalist consulting motion.
We do not ship an agent into production before identity, grounding scope, tool authorisation, audit telemetry and lifecycle posture are defined. The discipline that holds up under audit holds up under rollout.
RapidLogic™ aligns to Microsoft delivery frameworks including the Cloud Adoption Framework and Microsoft's Responsible AI Standard. Documentation is structured for procurement and audit review, not just internal delivery.
Selected engagements
Anonymised examples of typical Microsoft engagements. Named case studies are available under NDA on request.
Whole-of-agency Microsoft 365 tenant uplift with Copilot for Microsoft 365 readiness assessment. Includes data boundary configuration, Purview information protection alignment and Essential Eight Maturity Level uplift.
Dynamics 365 Customer Service implementation for a state department case management workflow. Delivered against RapidLogic™ with structured Discover, Configure and Deploy phases and a defined hypercare period.
Greenfield Azure landing zone build with hub-and-spoke topology, Microsoft Sentinel SIEM deployment, Defender for Cloud activation and Entra ID conditional access alignment to APRA CPS 234 expectations.
FAQs
Copilot Studio is a low-code agent surface that grounds against Microsoft 365 and Dataverse, with prebuilt connectors to line-of-business systems. It suits business-built agents where rapid iteration and a managed runtime matter. Azure AI Foundry is the developer surface: full control over model selection, retrieval, evaluation and orchestration. It suits agents that need bespoke grounding, custom tools or deep integration with your engineering pipelines. Most agencies and enterprises run both, with each agent placed on the surface that fits its scope and audience.
Yes, through Copilot Studio connectors or Azure AI Foundry tools. The authorisation boundary is defined in the agent specification at design time, not retrofitted later. Read-only agents (data lookup, summarisation) follow a different review track than write-capable agents (raise tickets, update records). We document the boundary in writing so audit and security teams can verify what each agent can and cannot do.
Six governance layers cover this on the page above: agent identity, grounding scope, tool authorisation, Responsible AI alignment, audit telemetry, and lifecycle management. Each layer is concrete: a configuration, a policy document, an integration with Purview / Sentinel / Defender. We do not treat agent governance as a tickbox at the end of the build.
Copilot Studio and Azure AI Foundry are available within Government tenants subject to the agency's own approval process. Government rollouts add ISM control alignment, Essential Eight maturity considerations, PSPF policy posture and data residency documented for audit. The Copilot for Government page walks the framework that applies equally to agentic AI; the agent specification adds the agent-level layers (identity, tool authorisation, lifecycle) on top.
Discovery runs two to four weeks. Pilot runs six to ten weeks against the approved agent specification. The agent surface (Copilot Studio vs. Azure AI Foundry) and grounding complexity drive the variation. Multi-agent scale follows the pilot's measured outcomes and is sized to your change capacity.
Copilot Studio is licensed per message-pack (a metered unit of agent interactions) and per author seat for builders. Azure AI Foundry is consumption-based on the underlying Azure OpenAI tokens plus any retrieval or hosting infrastructure. UHS Logic models the licence and consumption profile against the approved use cases at the discovery stage so cost is part of the design, not a surprise after rollout.
Subject to the agency's accreditation posture and the chosen agent surface. Agents are designed to ground only in approved data sources, with sensitivity labels enforced via Purview at grounding time. Where classification or sensitivity drives additional controls, we incorporate them into the design and verify against ISM and PSPF. Some agent scopes that involve highly classified material are out-of-scope by design and documented as such.
Each agent specification carries explicit Responsible AI considerations across fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. Out-of-scope use cases are documented. Evaluation harnesses (especially on Azure AI Foundry) include safety and groundedness scoring. Responsible AI is structured into the design, not a checkbox at the end.
Yes, and we sequence them deliberately. Copilot for Microsoft 365 rollout governance (data boundary, Purview, identity, approved use cases) is a foundation that agentic AI builds on. Where you have a Copilot rollout in flight, the agent governance layer extends it rather than duplicating it. See the Microsoft Copilot Rollout page for the rollout shape.
Adoption metrics are agreed at the start of the pilot, tied to the approved agent specification, and measured throughout. We track active usage by cohort, prompt success rates per approved use case, time-on-task reduction for the workflows the agent supports, and qualitative practitioner feedback. ROI framing depends on the workflow: time saved per task multiplied by audience size, error rate reduction, response-time improvement, or net new capability. We promise a measurement framework that produces a defensible ROI number, not a fixed number up front.
Tell us where you want agents to land and we will come back to you with a recommended discovery scope, an agent surface recommendation (Copilot Studio vs. Azure AI Foundry), the governance posture the framework will define, and a realistic pilot framing. One business day.
UHS Logic · Microsoft Solutions Partner