Azure AI Agent Service
We design, build, and deploy production AI agents on Azure AI Agent Service — wiring Code Interpreter, Function Calling, File Search, and Azure AI Search grounding into agents that execute multi-step tasks inside your Azure tenant.
How It Works
We analyse your target use case, map out the agent's required tools — Code Interpreter, File Search, Function Calling, Azure AI Search grounding — and design the thread lifecycle, state management strategy, and handoff logic.
We provision the Azure AI Agent Service environment, implement the agent definition, wire up all tools via the Agents SDK, connect Azure AI Search for grounded retrieval, and deploy the agent run infrastructure inside your Azure tenant.
We run the agent through a structured evaluation suite — tool invocation accuracy, response groundedness, token efficiency, and error-handling coverage — apply content safety filters, and hand over with full observability dashboards and Prompt Flow tracing.
What's Included
Azure AI Agent Service manages persistent threads — storing conversation history, intermediate tool outputs, and state across multiple turns without you building any session infrastructure. Every run is isolated and auditable.
Agents have native access to Code Interpreter (sandboxed Python execution), File Search (vector retrieval over uploaded documents), Function Calling (any external API or internal system), and Azure AI Search grounding for enterprise knowledge.
Wire multiple specialised agents together — a routing agent that dispatches to a coding agent, a document agent, and a data agent — with Azure AI Agent Service managing the inter-agent message passing and shared thread context.
All agent runs execute within your Azure tenant with Managed Identity authentication, private endpoints, no data leaving your compliance boundary, and Entra ID RBAC controlling who can create or invoke agents.
Every tool call, token consumption, and run step is captured in Azure AI Foundry's tracing dashboard — giving you latency breakdowns, cost attribution per agent run, and the complete reasoning trace for debugging.
Deploy agents through Prompt Flow pipelines for production-grade LLMOps — versioned prompt templates, A/B evaluation, CI/CD promotion gates, and automated regression testing before any agent update reaches production.
Who It's For
Organisations whose AI use cases require the agent to take actions — run code, query APIs, update records, retrieve documents — rather than simply generating text. Azure AI Agent Service makes this production-safe.
Engineering teams building agents that execute complex multi-step workflows — breaking down a user intent into a sequence of tool calls, evaluating results, and adapting the plan mid-run with full state continuity.
Financial services, healthcare, and legal organisations that need every AI action logged, every tool call recorded, and every output traceable to source data — within their own Azure compliance boundary.
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