Custom AI Agents
We build autonomous agents that operate within your enterprise systems reading data, executing tasks, calling APIs, and making decisions, all on-premise and under your governance policies.
Multi-Step Reasoning & Tool Use
Agents that decompose complex tasks into sequential steps, call external tools and APIs, validate intermediate results, and recover from failures — all without human intervention.
Enterprise System Integrations
Native connectors to your ERP (SAP, Oracle), DMS (SharePoint, OpenText), CRM (Salesforce, Dynamics), and internal REST or gRPC APIs. Agents act on real business data in real time.
RBAC & Governance
Every agent action is gated by your existing access policies. Role-based permissions define what data each agent can read, write, or escalate — with full audit trails for compliance.
Observability & Monitoring
Structured logging, distributed tracing, and alerting for every agent run. Full visibility into decision chains, latencies, tool calls, and failure modes — deployed on your monitoring stack.
What Is an AI Agent?
An AI agent is fundamentally different from a chatbot or a simple automation script. While a chatbot responds to questions and a script follows a fixed sequence of steps, an agent reasons about its task, decides which tools to use, evaluates intermediate results, and adapts its approach when something goes wrong — all within a single execution run.
In an enterprise context, this means an agent can receive a high-level instruction like "process this purchase order" and autonomously extract data from the document, validate it against your supplier database, check budget approval thresholds, create the order in your ERP system, and notify the relevant stakeholders — all without a human touching each step. When the agent encounters an ambiguity or an exception it cannot resolve, it escalates to a human with full context of what it has already done and why it stopped.
Use Cases
We build agents for specific enterprise workflows where the combination of data access, multi-step reasoning, and system integration creates measurable efficiency gains:
- —Procurement automation: Agents that receive purchase requests, validate against budget policies, source quotes from approved suppliers, and create purchase orders in SAP or Oracle — reducing procurement cycle time from days to hours.
- —Contract review and extraction: Agents that read incoming contracts, extract key terms (payment schedules, liability clauses, renewal dates), flag deviations from your standard terms, and populate your CLM system with structured data.
- —HR process automation: Onboarding agents that provision accounts across systems, schedule training sessions, prepare equipment requests, and generate compliance documentation — triggered by a single new-hire record in your HRIS.
- —Customer support triage: Agents that analyse incoming support tickets, classify by urgency and department, retrieve relevant knowledge base articles, and draft response suggestions — reducing first-response time and improving routing accuracy.
- —Financial reconciliation: Agents that match invoices against purchase orders and delivery receipts, flag discrepancies, and prepare exception reports for the finance team — handling hundreds of documents per day with consistent accuracy.
How We Build Agents
Every agent project follows a structured development process designed to deliver reliable, production-grade automation:
Workflow mapping
We document the current process end-to-end: who does what, which systems are involved, what the decision points are, and where exceptions occur. This becomes the specification the agent is built against.
Tool and integration design
We identify which external systems the agent needs to interact with and build typed tool interfaces for each — API connectors, database queries, file operations, notification channels. Each tool is tested independently before agent integration.
Agent logic development
The agent's reasoning chain is implemented using LangGraph or Pydantic AI, with explicit state management, error recovery paths, and human escalation triggers. We favour deterministic control flow over open-ended LLM reasoning for critical business decisions.
Testing and validation
Agents are tested against real historical data — not synthetic examples. We measure accuracy, latency, and edge-case handling against your actual workload patterns. Agents that do not meet accuracy thresholds are not deployed.
Security and Governance
AI agents that interact with production business systems require strict governance. Every agent we deploy is gated by your existing RBAC policies — the agent can only access data and perform actions that its assigned role permits. All agent actions are logged with full context: which tool was called, what data was accessed, what decision was made, and why. These audit logs integrate with your existing SIEM or compliance monitoring systems. For sensitive operations (financial transactions above a threshold, contract approvals, personnel actions), agents are configured with mandatory human-in-the-loop checkpoints where a human must explicitly approve before the agent proceeds.
Technology Stack
Frameworks and tools used in agent development