AI agents are stateful, goal-oriented software entities that do more than process static instructions. They sense changes by ingesting data from real-time event streams and APIs. They decide on the next best action using a combination of Large Language Models (LLMs), business logic, and learned models. They act by executing tasks through a library of tool integrations and API connectors. 

Use case: An agent that interprets client requests, automatically assigns tasks, monitors progress, and resolves detected issues, delivering outcomes end-to-end, escalating to human operators only for predefined exceptions

Our platform is built on a real-time data fabric with UDF. It ingests, cleanses, and correlates data from disparate sources into a common operational data model. This ensures that agents always work with a consistent, up-to-date, and accurate view of the business context.

Use case: The data foundation ingests streaming Point of Sale (POS) data from retail outlets, batch-processes historical sales data for seasonal trends, and correlates it with real-time supply chain logistics. This creates a unified, queryable Sales and Operations Planning (S&OP) dataset that agents can use to detect anomalies or predict stockouts.

Governance is enforced through a centralized policy engine and Role-Based Access Control (RBAC), defining granular permissions for what actions an agent can take and what data it can access. Transparency is achieved via immutable audit logs and a dedicated Explainability API that provides a step-by-step reasoning trail for every decision.

Use case: When an insurance underwriting agent adjusts a quote, it doesn’t just output the new price. It generates a structured JSON object containing the full ‘reasoning trail.’ This object details the specific data points considered, the business rules and models triggered (e.g., ‘Rule ID: RISK-7B’), and the confidence score for the decision, ensuring every action is fully machine-readable and auditable.

A central orchestration engine that manages the lifecycle and interaction of multiple agents. It uses a declarative workflow definition (e.g., YAML-based) to define complex, multi-agent processes. Agents communicate and share state via a high-throughput message bus (e.g., Kafka or similar), and human collaboration is integrated through bi-directional webhooks into tools like Slack, Microsoft Teams, and Jira. 

Use case: A customer support ticket triggers a workflow. Agent 1 (Sentiment Analyzer) is invoked, processes the ticket text, and publishes a ‘negative’ sentiment event to the message bus. The Orchestrator consumes this event and triggers Agent 2 (Recommendation Engine), which generates a proposed solution. The Orchestrator then uses a webhook to post the problem and proposed solution to a dedicated Slack channel, tagging the human support lead for approval. Once approved, the Orchestrator triggers Agent 3 (Comms Agent) to send the final response to the customer.

These foundational components, from the real-time data fabric to the orchestration engine, create an agentic AI platform that is capable of meeting today’s enterprise demands for scale, governance, and interoperability and is also architected to evolve for tomorrow’s challenges.