Orchestration architecture
Supervisor, pipeline, and peer-review topologies — chosen for your workflow, not copied from a demo.
Featured Solution
Collaborative Multi-Agent Systems
Specialized agents work together — researching, deciding, executing, and verifying — to solve workflows no single model can handle alone.
- 5–20+specialized agents per system
- 3×throughput vs. single-agent designs
- 100%of handoffs traced end-to-end
Capabilities
What production multi-agent systems require
Orchestrate collaborative AI agents that solve complex workflows across teams and systems.
Shared state and handoffs
Explicit state contracts between agents so context survives handoffs and failures are recoverable.
Specialist agent design
Narrow, well-evaluated agents for research, extraction, validation and action — each independently testable.
System-level evaluation
End-to-end evals that score the system's output, plus per-agent metrics to find the weak link fast.
How it works
Many specialists, one accountable system
Complex work doesn't fit one prompt. We decompose it into specialist agents with a supervisor that plans, delegates, verifies and retries — with every step observable.
Orchestration
The right topology for the job
Supervisor-worker for delegation, sequential pipelines for document flows, debate and reviewer patterns for quality-critical output. We design the coordination layer explicitly — including what happens when an agent fails.
- Supervisor agents that plan, route and verify work
- Deterministic checkpoints between probabilistic steps
- Retry, timeout and fallback policies per agent
Shared stateHandoffsRetriesTraces
State and handoffs
Context that survives the relay race
The hard part of multi-agent systems is not the agents — it's the handoffs. We define typed state contracts so each agent receives exactly what it needs and passes on exactly what it produced.
- Typed, versioned state schemas between agents
- Durable execution — resume from checkpoint, not from zero
- Shared memory scoped by task, team and permission
Invoice exception workflowRunning
✓Extract invoice fields1.1s
✓Match against PO0.6s
Resolve mismatch with agentrunning
·Post to ERPqueued
Quality control
Evaluate the system, not just the parts
Per-agent accuracy means little if the system's final output is wrong. We build eval harnesses that score end-to-end outcomes and per-hop quality, so you can see exactly where errors enter.
- End-to-end task success metrics with real fixtures
- Per-agent scorecards to isolate the weakest link
- Regression gates in CI before any prompt or model change ships
Groundedness
96%
Safety
100%
Task success
93%
Tone & format
91%
CI gate passedrelease promoted to production
Use cases
Workflows built for multiple agents
Document processing at scale
Extraction, validation, enrichment and posting as separate agents — auditable at each hop.
Research and due diligence
Parallel research agents with a synthesis agent that reconciles conflicts and cites sources.
Claims and case management
Intake, coverage check, fraud screen and resolution agents coordinated by a supervisor.
Content supply chains
Draft, fact-check, brand-check and localize — each a specialist with its own eval suite.
Code and data migrations
Analyzer, planner, executor and verifier agents working through large codebases systematically.
Complex customer resolution
Billing, entitlement and technical agents collaborating on cases no single bot could close.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
We'd built single agents that worked in isolation and fell apart when chained. The state contracts and system-level evals were what finally made the whole pipeline dependable.
Works with your stack
Orchestration on proven foundations
We build on mature orchestration frameworks and your existing infrastructure — with durable execution and full tracing from day one.
- LangGraph
- Temporal
- Anthropic Claude
- OpenAI
- AWS Bedrock
- Azure AI
- Kafka
- Redis
- Postgres
- OpenTelemetry
- MCP
- Kubernetes
FAQ
Common questions
When do I need multiple agents instead of one?
When a workflow has distinct skills (research vs. extraction vs. action), needs parallelism for throughput, or requires a reviewer step for quality. If one well-tooled agent can do the job, we'll tell you — multi-agent adds coordination cost that must earn its keep.
How do you prevent error cascades between agents?
Typed state contracts, validation at every handoff, and deterministic checkpoints. An agent that receives bad input rejects it early rather than compounding the error downstream.
How do you debug a system with many agents?
Every run produces a full trace: which agent did what, with which input, at what cost and latency. Per-agent scorecards plus end-to-end evals isolate the weak link in minutes instead of days.
What does this cost to run compared to a single agent?
More calls, but smaller and cheaper ones — specialists use smaller models where possible, and parallelism cuts wall-clock time. We routinely route sub-tasks to lighter models and reserve frontier models for the steps that need them.
Can this integrate with our existing single-agent work?
Yes. Existing agents typically become specialists inside the new topology, wrapped with state contracts and added to the shared eval harness.
Next step
Design your agent workforce
Bring us a workflow too complex for one agent — we'll architect the system that ships it.