AI gateway architecture for governed enterprise agents
Place model routing, fallback, provider policy, and usage controls alongside governed context and action APIs for enterprise agents.
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AI agent infrastructure
Give enterprise agents governed context, bounded actions, policy, provenance, and observability.
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Reviewed enterprise architecture guidance
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Observability for enterprise AI agents and model calls
Trace model requests, retrieval, tool use, business API dependencies, policy outcomes, cost signals, and failures without recording sensitive payloads by default.
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Security architecture for enterprise AI agents
Bound identities, context, tools, operations, outputs, and audit evidence across the path from a user request to enterprise systems.
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Fine-tuning versus RAG for enterprise systems
Choose between behavior adaptation and retrieved evidence by examining knowledge freshness, provenance, evaluation, privacy, and operating responsibility.
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How to evaluate GPT, Claude, and Gemini for enterprise workloads
Build a repeatable evaluation around your tasks, data policy, tool needs, latency, reliability, and cost instead of relying on a static benchmark table.
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A decision framework for local LLM deployment
Evaluate self-managed inference through capability, data handling, hardware, reliability, patching, observability, and team ownership.
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MCP in enterprise AI architecture
Use Model Context Protocol as a connection standard while keeping identity, authorization, business contracts, tool safety, and source operations explicit.
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Multi-model orchestration without hard-coded provider logic
Separate model selection from business context and tool contracts so providers can change without rewriting every agent workflow.
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Prompt caching as an operating decision
Use caching where repeated prompt prefixes, privacy controls, freshness, invalidation, and provider behavior make reuse safe and measurable.
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Production RAG architecture with governed enterprise context
Design retrieval around source authority, chunking, access control, evaluation, provenance, freshness, and failure behavior rather than a vector database alone.
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