AI Agent Development

AI agents that execute, not just suggest

Multi-agent workflows built on LangGraph that execute tasks, such as querying databases, calling APIs, drafting documents, and routing decisions, without a human touching every step.

Price anchor: $8K – $25K per system · Average ROI: 171%

What we build into every agent system

  • Multi-agent orchestration with LangGraph state machines
  • Human-in-the-loop approval gates for high-stakes decisions
  • Tool use: web search, code execution, database queries, API calls
  • Persistent memory and context across long-horizon tasks
  • Parallelized agent execution for throughput-critical workflows
  • Full observability with LangSmith tracing
  • Streaming responses for real-time UI feedback
  • Retry logic, fallback chains, and graceful error handling

Where we deploy agents

Legal

Legal research & brief drafting

Sales

Lead enrichment & outbound personalization

Finance

Financial analysis & report generation

Media

Content pipeline automation

SaaS

Customer support escalation routing

Operations

Supply chain exception handling

Frequently asked questions

What is an AI agent system?

An AI agent is software that uses an LLM to decide what actions to take, such as calling tools, querying databases, running code, or triggering workflows, to complete a goal autonomously. A multi-agent system coordinates multiple specialized agents to handle complex, multi-step business processes.

What does LangGraph give you that other frameworks don't?

LangGraph uses a state graph to model agent workflows, giving you explicit control over transitions, retries, human approvals, and parallel branches. Unlike agent loops in plain LangChain or AutoGen, LangGraph workflows are auditable, resumable, and production-safe.

How long does a typical agent build take?

A focused single-agent system takes 2–4 weeks. A multi-agent orchestration with integrations and UI typically runs 4–8 weeks. We phase the build so you see working demos after week 1.

Do you handle on-premise deployments?

Yes. We regularly deploy to private cloud or on-prem infrastructure using Docker and Kubernetes. Local LLM support (Ollama, LM Studio, vLLM) is available for data-sensitive deployments.

Ready to automate your most complex workflows?

Book a free architecture call. We'll map your workflow, identify the agent topology, and send you a written spec within 3 days.

Book a Free Architecture Call