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Agent Orchestration

Turn isolated agent demos into observable, recoverable workflows.

  • Specialist delegation
  • Scheduled workflows
  • Multi-agent handoffs

What gets in the way

  • Specialist agents duplicate work when handoffs and shared state are undefined.
  • Scheduled and event-driven work has no dependable recovery path.
  • Teams cannot see which agent owns the next decision or action.

What this is designed to produce

  • Explicit workflow graphs for delegation, queues, and handoffs.
  • Persistent state with retries, timeouts, and accountable escalation.
  • One trace across agents, models, tools, and human decisions.

Architecture, with its boundaries

  • Orchestrator

    Makes orchestrator an explicit, observable part of the Agent Orchestration system.Input boundary

  • Queue

    Makes queue an explicit, observable part of the Agent Orchestration system.Context boundary

  • State

    Makes state an explicit, observable part of the Agent Orchestration system.Decision boundary

  • Trace

    Makes trace an explicit, observable part of the Agent Orchestration system.Action boundary

How delivery is staged

  1. 01

    Discover

    Map the agent orchestration workflow, evidence, risks, owners, and baseline.
  2. 02

    Design

    Define boundaries, architecture, evaluation criteria, and human controls.
  3. 03

    Prove

    Validate one bounded workflow with representative data and accountable users.
  4. 04

    Operate

    Deploy with monitoring, recovery, change control, and an expansion backlog.

Questions that come up first

How does Agent Orchestration connect to our existing systems?

We map approved sources and actions through their supported APIs or controlled custom interfaces. A migration is not assumed; identity, permission, and transaction boundaries stay explicit.

Can we use our preferred model or cloud provider?

Yes. The architecture separates business context, evaluation, and tool policy from any single model. Provider choices remain subject to your security, residency, quality, and cost requirements.

Where does human approval remain?

Approval is designed around risk. External, sensitive, irreversible, low-confidence, or policy-exception actions stop for an accountable person before execution.

What is the first production milestone?

A bounded workflow with agreed inputs, controls, failure handling, evaluation criteria, and an owner. The target is dependable learning, not an inflated automation claim.

Start with clarity

If the business case isn’t there, we’ll tell you before you build.

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