Guide ยท design
An AI agent architecture is how you organise several agents, models and tools so they handle a full process together, end to end. The topic is moving out of research labs and large companies into small and mid-sized businesses, where the question is not only "does it work?" but "can I trust the system when nobody is watching?"
This guide covers the components that are non-negotiable and how Orkeste implements each one. For the definition, see what AI orchestration is.
Instead of trusting the whole chain to one model, you split the problem. One agent collects, another reconciles, a third drafts. Each agent has a clear scope, precise instructions and access limited to the tools it needs. Specialisation makes each step testable and the system more predictable.
The orchestrator starts agents at the right moment, passes context from one to the next and decides what happens when a step fails. Without that thread, agents fall back to being isolated demos.
An agent that knows neither your procedures nor the history of the case writes beside the point. Context means reference documents, what the previous agent found and the corrections already made.
This is what separates a production-grade architecture from an experiment. Sensitive actions (sending to a customer, writing to a financial tool, changing a record that holds personal data) go through an explicit validation. The person does not redo the work: they confirm, adjust or reject.
For every run: what went in, what came out, the actions taken, who approved them and what it cost. That is what makes the system auditable and lets you improve it.
A tool is down, a token expired, a reply is unexpected: decide in advance whether the chain stops and who is told. The same goes for spending: a cap per agent avoids surprises.
The scenario where agents coordinate themselves and act without interruption is elegant on paper and unpredictable in practice. When an agent gets something wrong, the action ships with no filter. The supervised alternative is automation in the right place: the system moves on its own through what is reversible and pauses for a quick validation on what matters. Our human-in-the-loop guide covers it in detail.
Three patterns come up most: in sequence, where each agent takes over from the previous one; in parallel, where several agents work on the same input; and on a condition, where an agent steps in only when the input calls for it. The trade-offs of each are covered in multi-agent orchestration explained.
Start with one repetitive, time-consuming process. Connect agents to your tools through connectors instead of rewiring everything. Place a human checkpoint on the final action. Measure the time saved and the rejection rate in validation. Once the first workflow is stable, extend to a second. Our step-by-step workflows give scoped examples.
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