Model Context Protocol lets external AI agents call FreightCortex tools natively. Here is why that matters — and what it unlocks for the freight intelligence stack.
Building Multi-Agent Workflows on Databricks (mosaic AI Agent Framework)
Multi-agent workflows native to the Lakehouse — designed, built, evaluated, and deployed on the Mosaic AI Agent Framework with typed tools and an evaluation harness.
Multi-Agent OSINT with a Critic and a Referee
A swarm of agents producing summaries is not analysis. Adding a critic and a referee changes what the system is. Here is how Aquil’s OSINT architecture is structured.
The Agent Factory: Planner, Executor, Critic, Referee
Most production agentic systems converge on the same architecture: a planner, an executor, a critic, and a referee. Here is the pattern, why it works, and how we apply it across industries.
Multi-Agent Quality: a New Architecture for the QMS
Traditional QMS systems are forms-and-rules engines. A multi-agent QMS is something different — and the difference matters operationally.
An AI Freight Analyst with 16 Tools
Most freight intelligence platforms add a chatbot. FreightCortex makes the analyst the center of the platform. Here is what an AI analyst with 16 callable tools actually does — and how it compares to a senior human analyst.
Multi-Agent Consensus for Systematic Literature Review
Single-LLM screening makes the SLR process faster but no more accurate. Multi-agent consensus screening — with four models, explanations, and disagreement detection — preserves PRISMA 2020 rigor.