R&D
Multi-Agentic Workflows
A single LLM call rarely solves a real workflow — quoting, onboarding, claims, content production. We are researching how a small swarm of specialised agents (planner, executor, critic, archivist) can hand work between themselves to finish the workflow without a human in the loop, and without compounding hallucinations.
Multi-Agentic Workflows
Each agent owns one capability and a narrow tool surface. A central planner decomposes the task, the executor calls tools, the critic checks output against the original prompt, and the archivist commits final state. This isolation is what keeps quality high at scale.
Multi-Agentic Workflows
Time-to-completion, hallucination rate, escalation rate (how often a human had to intervene), and end-to-end cost per workflow. Across the workflows we have tested so far, this architecture beats a single-agent baseline on every metric except first-token latency.
Multi-Agentic Workflows
At 85% we have an internal framework running 14 production workflows for design partners. The remaining work is observability — making it easy to see WHY an agent made a decision and replay the run end-to-end.
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