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AI agent & orchestration engineering is now at the core of what we do.

Orchestration

Orchestration engineering

An orchestrator that plans, routes, retries and knows when to stop, on workflows that survive failures.

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What you get

  • Planning & routing

    Work is split and sent to the right agent.

  • Durable runs

    Workflows resume after failures, not restart.

  • Budgets

    Cost, latency and step limits on every run.

  • Parallel agents

    Fan out, merge and verify results.

What a run looks like

support-triage.flow

The flow: a new ticket goes to the orchestrator, which sends it to a classifier, a research and a drafting agent; tickets below 0.8 confidence wait for human review before the reply is sent.

14:02:11 orchestrator → classifier  intent=billing p=0.94
14:02:13 research → crm.lookup    account found, 2 open invoices
14:02:17 drafting → evals         grounded, tone, pii: all pass

Where it fits

Every ticket triaged and drafted first

  1. 1 New ticket
  2. 2 Classify intent
  3. 3 Research account & docs
  4. 4 Draft reply
  5. 5 Human review if unsure

Tools it would use: helpdesk crm.lookup search_docs

Good questions

What is orchestration engineering?

Designing how agents, tools and people work together on one job: who plans, who executes, how results are checked, what happens when a step fails and what a run may cost.

Do we need several agents?

Often not at first. We start with the simplest design that works and add agents only when a task genuinely splits.

AI agent

AI agent engineering

Agents that act, not just answer.

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Reliability

Evals & observability

Know what your agents did, and why.

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Foundations

Cloud & DevOps

Infrastructure that’s boring to run.

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Have a process an agent should run?

Tell us about it. We’ll come back with how we’d approach it.

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