Build AI systems that think, plan, and execute
Design autonomous agent architectures that break down complex tasks, collaborate intelligently, and deliver reliable results at scale.
Why multi-agent?
- Automate complex workflows that require reasoning
- Break down large tasks into specialized agent roles
- Scale processing beyond single-model limitations
- Add human oversight at critical decision points
- Self-healing systems that retry and adapt
- Modular architecture: swap agents without rewriting
What's included
Agent Architecture Design
Design multi-agent topologies for your specific use case. Hub-and-spoke, hierarchical, or peer-to-peer patterns based on your requirements.
Workflow Orchestration
Build robust orchestration layers with retry logic, fallbacks, and human-in-the-loop checkpoints. Production-grade reliability.
Custom Agent Development
Develop specialized agents for your domain: research agents, coding agents, analysis agents, customer service agents, and more.
Use cases
Research Pipelines
Agents that search, analyze, summarize, and synthesize information from multiple sources.
Code Generation
Multi-agent systems that plan, write, review, and test code with human approval gates.
Document Processing
Extract, validate, transform, and route documents through intelligent agent workflows.
Customer Operations
Triage, respond, escalate, and follow up on customer inquiries autonomously.
When to talk
Your agent demo works, but nobody trusts it in production
This is usually an architecture, tooling, observability, or evaluation problem. Multi-agent design only helps when it reduces confusion rather than adding more moving parts.
One prompt is carrying too many jobs
Routing, retrieval, policy checks, tool calls, and final response generation often need separate contracts before they need separate agents.
You need a production review before adopting an agent framework
Framework choice matters less than state, traces, retries, permissions, and the ability to explain why a workflow failed.
Common questions
When should I use multi-agent systems vs a single LLM?
Multi-agent systems excel when: tasks require multiple specialized skills, you need deterministic sub-steps, the workflow benefits from parallel processing, or you need fine-grained control over each step. For simple Q&A or generation tasks, a single LLM is often sufficient.
What frameworks do you use?
Lightweight, custom orchestration is preferred over heavy frameworks. When frameworks make sense, LangGraph, CrewAI, and AutoGen are used. The choice depends on team familiarity and long-term maintenance needs.
How do you handle agent failures?
Every production system includes: retry logic with exponential backoff, fallback agents for critical paths, human escalation for edge cases, and comprehensive logging for debugging. No agent system should fail silently.
Can multi-agent systems work with our existing tools?
Yes. Agents can be given tool-use capabilities to interact with APIs, databases, file systems, and internal services. Integration layers are designed to connect agents to existing infrastructure.