Multi-Agent Workflows
Complex business processes require multiple specializations. We build multi-agent systems where narrow, specialized AI agents (e.g., a Researcher, an Analyst, and a Reviewer) collaborate, critique each other's work, and hand off tasks to complete massive workflows that a single LLM cannot handle.
Core Features
Specialized Roles
Deploying distinct agents with different system prompts, tools, and models tailored exactly to their specific sub-task.
Agentic Peer Review
Implementing 'Critic' agents that evaluate the output of 'Generator' agents against strict rubrics before allowing the workflow to proceed.
Stateful Handoffs
Robust state management that ensures context, variables, and history are passed cleanly between agents without data loss.
Parallel Execution
Architecting workflows where multiple agents gather data simultaneously before funneling back to a synthesis agent.
Our Process
Workflow Deconstruction
Week 1-2Breaking down your complex human workflow into distinct, non-overlapping tasks suitable for specialized agents.
Agent Specialization
Week 3-4Engineering the specific prompts and capabilities for each individual agent (e.g., Researcher Agent vs. QA Agent).
Routing & Orchestration Logic
Week 5-6Using frameworks like LangGraph to build the cyclic graphs that control how agents pass messages and delegate tasks.
Multi-Agent Simulation
Week 7-8Running the collaborative team through simulations to observe how they negotiate, critique, and resolve deadlocks.
UI & Production Deployment
Week 9-10Building an interface where human managers can observe the agents working, inject feedback, and approve final outputs.
Technologies We Use
FAQ
Why use multiple agents instead of one smart prompt?
Do the agents talk to each other?
Isn't it expensive to have multiple AI models running?
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