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Pro-Code Agents on Azure AI Foundry, Azure OpenAI & Claude

AI Agents & Multi-Agent Systems

When a workflow needs deep multi-step reasoning, custom orchestration or product-grade control, low-code runs out. We design, build and run AI agents and multi-agent systems in code, with evaluation, tracing and human approval built in from the first commit.

The engineering end of enterprise AI

What are AI agents and multi-agent systems?

An AI agent is a system that plans work, calls tools, checks its own output and hands off to people or to other agents, with humans approving the steps that matter. A multi-agent system puts several of them to work on one problem: a planner decomposing the job, workers executing in parallel, a reviewer challenging the output before anything reaches a customer. This is the engineering end of our AI practice, and it is where the workflows too heavy for a chatbot builder get solved.

Grounding does the heavy lifting. Most of what we build sits on private generative AI foundations: knowledge assistants that answer from your documents with citations, retrieval pipelines over governed data, document intelligence that turns unstructured contracts, claims and correspondence into structured decisions, and Azure OpenAI integrations that put all of it inside your security boundary rather than on someone else's infrastructure.

On orchestration we are deliberately framework-agnostic. We build with Azure AI Foundry Agent Service where a managed, Azure-native runtime with enterprise governance is the right call, with LangChain and LangGraph where a workflow needs full control of the reasoning graph, and with CrewAI where role-based agent crews get a team productive fastest. The framework is a consequence of the workload, never the starting point, and the comparison below is the conversation we have with every client.

Models follow the same logic. We run Azure OpenAI models inside Foundry for estates that need Azure-native controls, with the platform side covered in depth by our Azure AI Foundry practice, and we build on Claude as a registered Anthropic partner where complex, multi-step reasoning and tool use earn their keep. Every system ships with evaluation harnesses, tracing and human-in-the-loop approval gates, because an agent nobody can audit is an incident report waiting to be written.

Two near neighbours worth separating. If a well-bounded agent over Microsoft data would do the job, you want Copilot Studio, covered by our Microsoft Copilot consultancy, and you will be live in weeks. If you want agents building your software rather than running your workflows, that is agentic development. We deliver all three, so the recommendation you get is the right route, not the one we happen to sell.

Agent stack we build on: Azure AI Foundry, Claude, LangChain and CrewAI

Built on Azure AI Foundry, Azure OpenAI and Claude

Data & AI on Azure
Data & AI on Azure
Digital & App Innovation on Azure
Digital & App Innovation on Azure
Microsoft Solutions Partner
Microsoft Solutions Partner
What We Deliver

AI agent capabilities we deliver

Azure OpenAI Solution Design and Deployment

Design and deploy enterprise-grade solutions powered by Azure OpenAI, tailored to your business processes and integrated securely into your existing technology landscape.

Private Generative AI and Knowledge Assistants

Build private, secure generative AI architectures and knowledge assistants that answer from your own data with citations, keeping sensitive content inside your boundary.

  • Retrieval pipelines over governed data with private networking
  • Citations users can check, so trust is earned rather than assumed
  • Entra ID integration and role-trimmed answers
  • Prompt and response logging for audit and improvement

Multi-Agent Orchestration

Design and build multi-agent systems with planner, worker and reviewer patterns, using Azure AI Foundry Agent Service, LangChain, LangGraph or CrewAI as the workload demands.

  • Planner, worker and reviewer patterns matched to the workflow
  • Foundry Agent Service, LangChain, LangGraph and CrewAI expertise
  • Agent-to-agent handoffs with full decision traceability
  • Human approval gates on every consequential action

Document Intelligence

Turn contracts, claims, invoices and correspondence into structured, decision-ready data with agents that read, extract, validate and route, including our Xtract accelerator for document-heavy workloads.

Evaluation, Tracing and Agent Governance

Every agent ships with an evaluation harness, golden datasets, regression gates and tracing, so behaviour is measured before launch and monitored after it.

  • Golden datasets and regression gates before anything ships
  • Tracing and observability across every agent decision
  • Red teaming for prompt injection and data exfiltration
  • An owner and an off switch for every agent in production

Azure AI Foundry Platform Engineering

Stand up the platform your agents run on: Foundry hubs and projects, RBAC, private endpoints, model routing and cost management, covered in depth by our Azure AI Foundry practice.

By the Numbers

Proven enterprise delivery

0

Client retention across our engagements

0

Microsoft certifications across the team

0

Microsoft MVPs on staff

0

Industries with multi-agent programmes in flight today

100%

Agents shipped with human approval gates on consequential actions

0

Agents in production without evaluation, tracing and an owner

3

Orchestration frameworks we build on: Foundry Agent Service, LangChain, CrewAI

2

Model stacks in production: Azure OpenAI and Claude

Common Use Cases

Where AI agents earn their keep

Claims and Underwriting Document Processing

Agents read claims packs, extract and validate the facts, flag inconsistencies and route decisions to humans, cutting cycle times in document-heavy insurance and finance workflows.

Multi-System Agentic Automation

Workflows that span CRM, ERP and line-of-business systems, where an agent plans the steps, calls each system and asks a human before anything consequential happens.

Enterprise Knowledge Assistants

Assistants that answer from policies, contracts and institutional knowledge with citations, deployed privately so sensitive content never leaves your boundary.

AI Features Inside Your Products

Agentic capability embedded in your own software and services, engineered with the evaluation and cost controls a customer-facing product demands.

Research and Analysis Copilots

Multi-agent research workflows that gather, verify and synthesise information from many sources, with a reviewer agent challenging the output before a human sees it.

Risk and Compliance Agents

Agents that monitor transactions, communications or cases against policy, escalating what matters with a full decision trail for the second line to audit.

How We Work

How we deliver production agents

  1. 01 Step

    Use-Case Scoring and Route Selection

    We score the candidate workflow on value, feasibility and risk, and confirm it genuinely needs a pro-code agent. If Copilot Studio would do the job, we say so before any code is written.

  2. 02 Step

    Architecture and Grounding Design

    We design the agent architecture, choose the orchestration framework and model stack, and build the retrieval and grounding layer over your governed data.

  3. 03 Step

    Build with an Evaluation Harness

    Agents are built against golden datasets and regression gates from the first sprint, so quality is measured continuously rather than discovered at launch.

  4. 04 Step

    Human-in-the-Loop Hardening

    We design the approval gates, escalation paths and red-team the system for prompt injection and data leakage before real users and real data arrive.

  5. 05 Step

    Production Monitoring and Governance

    Tracing, cost monitoring and behavioural alerts run from day one in production, with an owner who can tune the system or switch it off.

Orchestration

Foundry Agent Service, LangChain or CrewAI?

Framework choice is a consequence of the workload, not a matter of taste. These are the three we reach for most, and the differences that actually matter in production.

Foundry Agent Service, LangChain or CrewAI?
Criteria Azure AI Foundry Agent Service LangChain / LangGraph CrewAI
Best for Azure-native estates wanting a managed agent runtimeComplex workflows needing full control of the reasoning graphRole-based agent crews stood up quickly
Orchestration model Managed threads, tools and handoffs inside FoundryExplicit graphs: you own every node, edge and state transitionCrews of role-defined agents collaborating on tasks
Governance surface Entra ID, private networking and Azure policy out of the boxWhatever you engineer, with LangSmith tracing availableLightweight by default, hardened by your platform
Engineering weight Lower: the platform carries the infrastructureHigher: maximum control carries maximum responsibilityLow to start, growing with the ambition of the crew
Lock-in shape Azure-committed, model-flexible within FoundryFramework-portable across clouds and model providersPortable, with a smaller enterprise track record

We build on all three, so the recommendation follows the workload: Foundry Agent Service when Azure-native governance should do the heavy lifting, LangGraph when the reasoning graph is the product, CrewAI when role-based crews fit the problem and speed matters. Route selection is the first artefact of every engagement, and our AI strategy practice will score the use case before anyone argues about frameworks.

FAQ

AI agents FAQs

What is a multi-agent system?

Several AI agents collaborating on one problem: typically a planner decomposing the work, workers executing steps in parallel and a reviewer challenging the output before anything reaches a person or a customer. The pattern suits workflows too heavy for a single agent, and it demands proper orchestration, tracing and human approval gates.

How do you keep agents from hallucinating?

We ground agents in your content using retrieval over curated SharePoint, Fabric or bespoke vector stores, constrain answers with prompt engineering and tool use, log every interaction, and add human-in-the-loop patterns for sensitive actions.

Is this safe for regulated data?

Yes, when designed well. We use private networking, Purview sensitivity labels, Entra identity, data residency configuration and prompt / response logging. We have delivered agents in financial services, healthcare and public sector.

How do you measure success?

We baseline the target workflow before build (volumes, handling time, deflection, CSAT) and measure impact after go-live. Typical outcomes include 40–70% deflection on routine queries, 20–50% handling-time reduction and measurable user satisfaction gains.

Foundry Agent Service, LangChain or CrewAI: how do you choose?

The framework follows the workload. Azure AI Foundry Agent Service when a managed, Azure-native runtime with enterprise governance is the right call; LangChain and LangGraph when the workflow needs full control of the reasoning graph; CrewAI when role-based agent crews fit the problem and speed matters. We build on all three, so the recommendation is not shaped by what we happen to sell.

Which models do you build agents on?

Azure OpenAI models inside Azure AI Foundry for estates that need Azure-native controls, and Claude, as a registered Anthropic partner, where complex multi-step reasoning and tool use earn their keep. Model choice is evaluated per use case against quality, cost and governance requirements, not decided by habit.

Our Clients

Trusted by

Glassmoon
Lanware
Micheldever Tyre Services
Midwich
Mount Anvil
Nuevo Partners
Pro Global
Seras Energy
Skanska
Ocean Conservation Trust
WA Comms
Glassmoon
Lanware
Micheldever Tyre Services
Midwich
Mount Anvil
Nuevo Partners
Pro Global
Seras Energy
Skanska
Ocean Conservation Trust
WA Comms
Client Voices

Hear from our clients

Video Stories

Skanska testimonial video
Skanska

David, Skanska

Project/Programme Manager

Mount Anvil testimonial video
Mount Anvil

Mike, Mount Anvil

Head of Technology Applications

Testimonials

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Bring the process that eats your team's week. We will map it to an agent design, name the guardrails it needs and give you an honest estimate of what production takes.

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