Foundation Layer

Agentic Orchestration Platform

Coordinates multi-step, multi-agent workflows across tools, data sources and human approval points.

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The problem

What breaks without this

  • Point-solution chatbots can answer questions but cannot execute multi-step processes that span systems.
  • Chaining models and tools with custom, one-off code produces brittle, hard-to-audit workflows.
  • Handoffs between agents and humans are ad hoc, with no consistent approval or escalation pattern.
In one paragraph

Provides the runtime for planning, sequencing and supervising AI agents that call tools, query systems of record and hand off to one another, so Application layer agents behave predictably inside long-running business processes.

Capabilities

What it does

Declarative workflow definitions for task decomposition, tool calling and conditional branching.

Native support for the Model Context Protocol (MCP) for tool and data-source connections, and Agent-to-Agent (A2A) messaging for multi-agent handoff.

Human-in-the-loop checkpoints for approval, correction or escalation before high-impact actions.

Execution tracing that records every tool call, decision and intermediate state for replay and audit.

Retry, timeout and fallback handling for unreliable tools or downstream systems.

How it works

From request to result

01

Define

A workflow is defined as a sequence of tasks, tools and decision points, referencing MCP-exposed tools and data sources.

02

Plan

The orchestrator decomposes a request into an ordered plan, selecting tools and, where relevant, other agents via A2A messaging.

03

Execute

Steps run with tracing, retries and approval checkpoints as configured.

04

Supervise

Long-running or exception cases surface to a human queue with full context for resolution.

Governance & security

Built to be audited, not just used

  • Every agent action against a system of record is scoped to an explicit permission set, never inherited implicitly.
  • High-impact actions — payments, record deletion, external communication — require a configured approval gate.
  • Full execution traces support post-incident review and regulatory audit requests.
Integrates with

Fits existing infrastructure

Enterprise AI Gateway RAG & Knowledge Retrieval Ticketing / ITSM systems RPA tooling Core business systems (ERP / CRM) via MCP connectors

Exposed to agents through the Model Context Protocol (MCP) for tool and data access, and Agent-to-Agent (A2A) messaging for multi-agent handoff — so third-party and custom agents can integrate without proprietary connectors.

What changes

Outcomes to expect

Qualitative, directional outcomes. We do not publish unverified performance figures — see the case studies section for engagement-specific, authorized results.

  • Multi-step processes that previously required manual coordination across systems run as a supervised, auditable workflow.
  • New agents are composed from existing tools and other agents rather than built from scratch.
  • Approval and escalation behavior is consistent across every workflow, not defined per project.
Related

Works alongside

Foundation

Enterprise AI Gateway

A single, governed entry point for every model, prompt and AI service used across the organization.

 Foundation

RAG & Knowledge Retrieval

Grounds every AI response in an organization's own documents, records and policies.

 Developer & Engineering

AI Agent Builder

A low-code environment for composing new agents from governed Foundation-layer building blocks.

 Operations & Analytics

AI Workflow Automation

Automates multi-step business processes end to end, with human checkpoints where judgment is required.

Evaluate Agentic Orchestration Platform for your environment

Talk to an architect about integration into your existing stack, or request a scoped demo against a representative use case.

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