AI Agents for Business Processes
Build AI agents for cross-system business workflows with approvals, audit trail, and production guardrails where they matter.
The problem
Business processes usually do not fail inside one system. They fail in the handoff between CRM, ERP, ticketing, email, documents, and approval tools. People collect context by hand, copy the same data from place to place, and wait for someone to push the next step forward.
That work is a good fit for AI agents. A well-scoped agent does not replace the whole process. It takes responsibility for the repetitive part: gather case context, apply rules, prepare the next action, update the right systems, and bring in a person when the case needs judgment.
What we build
We deliver cross-system AI agents for one clearly defined business process at a time. Typical starting points include service request intake, order exception handling, customer onboarding, document-driven case work, and internal operations workflows.
Example Workflow
One agent, one process, many systems
A lightweight view of a business-process agent: it gathers context, follows rules, asks for approval when needed, and keeps the connected systems in sync.
1. Trigger
A case enters the process
An order exception, service request, or document starts the workflow.
2. Agent
Context is gathered across systems
The agent collects the case state, customer data, policy rules, and missing inputs.
3. Control
Rules and approvals are applied
Low-risk steps move forward. Sensitive actions are routed to a person.
4. Outcome
Systems are updated with a full audit trail
Tickets, records, and notifications stay in sync so the next team sees the same state.
CRM
Customer context
ERP
Order and finance data
Ticketing
Case queue and status
Docs
Policies and attachments
Agent Core
Cross-system case worker
Reads the case, checks the rules, prepares the action, and routes the work to the right place.
In practice, the work usually includes:
- one target process with a clear trigger, decision points, and end state
- integrations to the systems that hold the case data, business rules, and action endpoints
- agent logic for context gathering, decision support, task execution, and exception routing
- human approval checkpoints for financial, regulatory, or customer-impacting actions
- monitoring, audit trail, and handover documentation for production use
The point is to keep the scope concrete. Instead of a generic AI experiment, the work stays tied to one real process, the systems around it, and the operating rules the agent must follow.
How we work
We start with one process that is painful enough to matter and bounded enough to deliver safely. Then we map the systems, actors, business rules, failure paths, and approvals around that process. This gives the agent a clear operating boundary before we build anything.
From there, we implement the solution against real APIs, events, documents, queues, and user roles. We test with real cases, including failure and edge scenarios, before rollout. Your team gets the architecture, code, deployment setup, monitoring, and runbook needed to operate and extend the service.
Key Technologies
- Microsoft AI Foundry
- Azure OpenAI Service
- Semantic Kernel
- Azure Functions and Durable Functions
- Azure Logic Apps
- Azure Service Bus
- Azure API Management
- Microsoft Entra ID
- Model Context Protocol (MCP)
Delivery Foundations
- Process mapping with clear agent boundaries and case states
- Least-privilege tool access for every connected system
- Human approval checkpoints and exception routing
- Idempotency, retries, and compensation for multi-step actions
- End-to-end action logs, case history, and operational monitoring
- Evaluation with real process cases, failure paths, and policy tests
- Production runbooks, support model, and knowledge transfer