Build multi-agent AI systems that collaborate, reason, and execute. We develop production AutoGen applications where specialized agents — coders, analysts, reviewers, planners — work together through structured conversations to solve complex tasks.
Proof-First Delivery
What We Offer
Each module is designed as a production block with integration boundaries, governance hooks, and measurable outcomes.
Design agent teams with specialized roles — AssistantAgent, UserProxyAgent, and custom agents. Define conversation patterns, termination conditions, and human-in-the-loop checkpoints for safe, effective collaboration.
AutoGen agents that write, review, test, and debug code. Automated code generation with built-in review cycles — the coder writes, the reviewer checks, and the executor runs tests until quality standards are met.
Multi-agent data analysis — one agent writes SQL/Python queries, another executes them, a third interprets results and generates visualizations. Iterative analysis that refines itself based on intermediate findings.
Agent teams that research topics, gather data from multiple sources, synthesize findings, and generate structured reports. Web search, document analysis, and fact-checking through agent collaboration.
Connect AutoGen agents to external tools — APIs, databases, file systems, web browsers, and custom business logic. Function calling that gives agents real-world capabilities beyond conversation.
Deploy AutoGen systems with proper error handling, conversation logging, cost monitoring, safety guardrails, and scalable infrastructure. Production-ready, not notebook-only.
We design agent teams that actually work — proper role separation, conversation flow control, and termination conditions that prevent infinite loops and runaway costs.
Human-in-the-loop approval for code execution, cost budgets per conversation, sandboxed execution environments, and content filtering. Multi-agent systems that are safe for enterprise deployment.
AutoGen demos are easy. Production systems are hard. We handle conversation persistence, error recovery, concurrent execution, logging, and monitoring for reliable 24/7 operation.
We recommend AutoGen when conversational multi-agent patterns fit your use case. For other patterns, we suggest LangGraph or CrewAI. No framework bias — just the right tool for your problem.
Delivery Proof
Selected engagements that show architecture depth, execution quality, and measurable business impact.
Delivery Advantages
Design agent teams with specialized roles — AssistantAgent, UserProxyAgent, and custom agents. Define conversation patterns, termination conditions, and human-in-the-loop checkpoints for safe, effective collaboration.
AutoGen agents that write, review, test, and debug code. Automated code generation with built-in review cycles — the coder writes, the reviewer checks, and the executor runs tests until quality standards are met.
Multi-agent data analysis — one agent writes SQL/Python queries, another executes them, a third interprets results and generates visualizations. Iterative analysis that refines itself based on intermediate findings.
Agent teams that research topics, gather data from multiple sources, synthesize findings, and generate structured reports. Web search, document analysis, and fact-checking through agent collaboration.
FAQ
Tell us about your complex task or workflow — we'll design an AutoGen agent team that collaborates effectively to deliver reliable results.