Agentic AI in action

How Multi-Agent
Orchestration Works

Each node is an agent. Each arrow is a call. This is how production AI runs.

Most enterprise AI starts as a single model: one prompt in, one answer out. Production systems do not stay that way.

Specialist agents coordinate on complex work. An orchestrator routes tasks to the right agent. Each agent uses the right model for the job. Smaller models handle routine steps. Stronger models handle reasoning. Humans stay in the loop when judgment is required.

Full transparency. Graceful recovery. Agents that collaborate, escalate, and improve through feedback.

These are the exact architectures we deploy for clients. 8 to 12 weeks to working software on your infrastructure.

Talk to an EngineerSee Industries

Why this matters

Single-model AI hits walls. Multi-agent orchestration breaks through.

This is how AI scales. Think big, start small. Prove value fast. Scale what works.

01
Specialists handle what they are good at

Rules agents handle rules. Analysis agents handle analysis. No single model pretending to be everything.

02
Every decision is traceable and auditable

Full audit trail from input to output. See which agent made which call and why.

03
Failures recover gracefully

One agent fails, others continue. Automatic retry, fallback, and exception handling built in.

04
Humans supervise exceptions, not every transaction

95% flows through automatically. Your team reviews the 5% that actually needs judgment.

Demo

Multi-Agent Orchestration in Action

Pick an industry. Choose a scenario. Hit Run. Watch agents coordinate in real time: route, decide, escalate.

These are the exact architectures we deploy for clients.

Finance
Transaction Monitoring Platform by paterhn
Real-time financial crime detection — 9 agents processing 50K txns/hour with human SAR review
Each node is an agent. Each arrow is a call. This is how production AI actually runs.
SCENARIO
Pick an industry, choose a scenario, hit Run.
INPUT PAYLOAD
volume50,000 txns/hour
transactionWire Transfer CHF 8,400
originatorAcme Corp (known customer)
beneficiarySupplier — 3-year relationship
patternMatches historical baseline
sanctionsClear
STATUS
Idle
Pending
Running
Complete
Human Review
ORCH
Orchestrator
ING
Ingest
RUL
Rules
BEH
Behavioral
NET
Network
ENS
Ensemble
ADP
Adaptive
SAR
SAR Queue
EVD
Evidence
Orchestrator
Decision
Data
Analysis
Human

Synthetic data for privacy. Architecture identical to live client deployments.

6 Components

How the architecture works

Each component handles a specific responsibility. Together, they create production-grade AI.

The brain of the system. Receives incoming requests, understands intent, and routes to the right specialist. No single model tries to do everything. The orchestrator delegates.
Each agent owns one domain. Rules agents check compliance. Analysis agents process data. Decision agents weigh outcomes. Separation means each can be tuned, tested, and improved independently.
Not every task needs GPT-4. Routine checks go to fast, cheap models. Complex reasoning goes to stronger ones. You control the cost curve. 40 to 60% savings without sacrificing quality.
Input, output, which agent, which model, what confidence, how long. Every call traced. When regulators ask why, you have the answer. When something breaks, you know where.
Agents handle the 95%. Edge cases, high-risk decisions, and novel situations go to your team. With full context: what the agents tried, what they found, why they escalated.
Measure what matters. Track accuracy, latency, cost per decision. Feed outcomes back into the system. Models improve from real production data, not synthetic benchmarks.
Modular. Auditable. Built to scale.
Questions & Answers

FAQs

Everything you need to know about how Agentic AI works.

What is multi-agent orchestration?

Specialist AI agents coordinating on complex tasks. An orchestrator routes work, each agent handles its domain, humans supervise exceptions.

How is this different from a single AI model?

Single models try to do everything. Multi-agent systems use specialists. Each agent handles what it is good at. Full audit trail, graceful recovery, smart escalation.

Can I see it working?

Yes. Pick an industry above, choose a scenario, watch agents coordinate in real time. These are the exact architectures we deploy for clients.

How long does deployment take?

8 to 12 weeks for a working system on your infrastructure. Production-grade software with full handover.

Do we own the system?

Yes. Code, models, weights, documentation: all yours. No licensing fees, no lock-in.

What industries does this work for?

Finance, manufacturing, supply chain, pharma, and regtech. Any domain with complex workflows or decisions that need speed and accuracy.

Where does this run?

On your infrastructure: cloud, on-prem, or hybrid. We support Swiss and EU data residency when required.

8–12 weeks

From kickoff to production system. Not a demo, a working system on your infrastructure.

20+ years shipping ML100% IP ownershipNo vendor lock-inYour infrastructure

Let's Build.

Talk to an Engineer