Peer into the engineering behind Kaman — from hierarchical memory architectures to privacy-preserving collective intelligence. Published by our research team.
Eight Interlocking Subsystems from LLM Routing to Cryptographic Accountability
We present the end-to-end architecture of the Kaman platform, an enterprise system for deploying autonomous AI agents that reason over organizational data, invoke external tools, communicate across channels, and learn collectively. The platform comprises eight co-designed layers — LLM routing, context management, tool discovery, plugin integration, a version-native data lake, hierarchical memory, omnichannel communication, and a cryptographic security fabric — each described in a companion paper and unified here into a coherent whole. We describe the design principles, inter-layer contracts, deployment architecture, and performance characteristics observed across production workloads.
Download PDFAutonomous AI agents that invoke tools, access data, send messages, and modify configurations create an accountability gap: organizations cannot prove what an agent did, when, or w...
We present KMMS (Kaman Memory Management System), a five-layer hierarchical memory architecture that enables AI agents to maintain coherent long-term context across enterprise work...
We present a unified plugin architecture based on the Model Context Protocol (MCP) that enables enterprise AI agents to integrate with arbitrary external systems through a standard...
We introduce KDL (Kaman Data Lake), a modern lakehouse architecture that combines embedded columnar analytics, object storage, and native version control to serve AI agent workload...
We present an omnichannel communication architecture that enables autonomous AI agents to operate seamlessly across 15+ communication platforms including WhatsApp, Slack, Telegram,...
Static tool binding in LLM-based agents scales poorly: binding N tools consumes O(N) context tokens, and for enterprise agents with hundreds of available tools this can exhaust 30–...
Enterprise AI platforms must integrate multiple LLM providers — OpenAI, Anthropic, Google, Groq, Azure, AWS Bedrock — each with distinct cost, latency, and capability profiles. We ...
Long-running agent sessions that interleave multi-turn dialogue, tool invocations, and chain-of-thought reasoning present a fundamental resource management challenge: the finite co...
Hierarchical memory systems, collective learning protocols, and cross-deployment knowledge sharing.
Model Context Protocol integration, transport abstraction, sandboxed execution, and credential management.
Version-native data lakes, columnar analytics, time travel queries, and real-time ingestion pipelines.
Semantic search for dynamic tool binding, context-aware caching, and loop detection in LLM agents.
Omnichannel agent deployment with session continuity, streaming responses, and platform-specific adaptation.
Ed25519 signing, SAR hash chains, Merkle root anchoring, plugin integrity verification, and compliance dashboards.
End-to-end system design with eight co-designed layers, inter-layer contracts, and deployment architecture.
Multi-provider abstraction, adaptive complexity classification, cost-optimized inference, and automatic failover.
Tiered compression, pre-flight budget checking, sub-agent isolation, and middleware-driven augmentation.
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