Agent Orchestration — multi-agent setups for multi-step workflows.
AI agent orchestration suits processes that consist of several distinct steps. A Planner agent receives the request, Execution agents handle specialised subtasks, and a QA agent reviews the output before it leaves the system. Deployment can be on Azure OpenAI EU regions or on customer-owned infrastructure, depending on your data residency needs.
We build multi-agent setups (LangGraph, AutoGen) for complex business workflows. Deployment in Azure OpenAI EU regions or on customer-owned servers, based on your data residency requirements.
Specialised agents handle different steps of a longer workflow.
Deploy via Azure OpenAI EU regions or on the customer's own infrastructure.
Clear agent communication paths with end-conditions and recursion limits.
Agent anatomy
Multi-agent team structure
One person rarely handles backend code, marketing design, sales, and legal advice at the same time — each role specialises. The same logic applies when designing AI agent teams: split responsibility by step, not by person.
The first agent in the chain. Receives the request, breaks it into smaller tasks, and dispatches them to execution agents based on their specialisation.
Specialised agents that perform concrete tasks: SQL queries against a database, document search via RAG (e.g. Pinecone, pgvector), or ERP API calls (e.g. Rivilė, Directo).
A review agent. Before a final document leaves the system (e.g. a quote going to a client), this agent checks the earlier agents' output against your rules.
Integration with the systems your team already uses: Jira, Confluence, SharePoint, Slack, Teams. Agents pull context and post results through your existing channels.
Deployment options
Data residency and GDPR
Model calls run in EU regions (Stockholm or Frankfurt). On the Azure OpenAI business tier, your prompts and documents are contractually excluded from model training.
→ Suitable for GDPR-sensitive workflows.
We deploy open-source models (e.g. Llama 3, Mixtral) on your own servers. Datasets stay within your internal network behind your firewall.
→ Suitable for organisations with strict data residency requirements.
Pricing
Pricing by system complexity
Exact price and architecture are fixed after a free audit. Book the audit →
Questions
Frequently asked questions
- What is the difference between a single AI agent and agent orchestration?
- A single agent handles one task end-to-end (e.g., managing an inbox). Orchestration means deploying a team of agents where each handles one step of a longer flow: a front-line agent receives the request, a data agent runs the lookup, a QA agent checks the output before it leaves the system. Useful when a process has several distinct steps.
- Where will my data be stored and what is the EU residency option?
- We can deploy via Azure OpenAI in EU regions (Frankfurt or Stockholm) or on customer-owned infrastructure with open-source models (e.g. Llama 3, Mixtral). Business-tier APIs contractually exclude customer data from model training. The exact deployment depends on your data classification and requirements.
- How is reliability maintained when multiple agents communicate?
- We use LangGraph or similar tools to map clear execution flows. End-conditions and recursion limits prevent runaway loops. For sensitive actions we add a Human-In-The-Loop (HITL) checkpoint before the agent commits.
Request an AI agent orchestration quote
Send a short description of the workflow, systems, and decisions you want to automate. We will review whether a multi-agent setup is the right fit and suggest a practical next step.
- ✅Free 30-min audit
- ⚡Response within 24 hours
- 📋Firm pricing quote
- 🔒GDPR compliant
- 🤝Zero initial obligations