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Tagged: enterprise-ai
Training Enterprise AI Agents: Choosing Between SFT, DPO, GRPO and RAG
Training enterprise AI agents: when to use SFT, DPO, GRPO, LoRA or plain RAG, and how to ship the result as a pinned, evaluated and reversible release.
50 min readThree Ways Production RAG Fails: Fragmented Context, Drift and Misuse
Three ways production RAG fails: fragmented context from chunking, drift as the knowledge base grows, and misuse despite good retrieval, with layer-level fixes.
11 min readAI Architecture for Production Agents: Failure Modes and Control Layers
A layer-by-layer architecture guide for production agent systems: failure modes, guardrails, evals, observability, cost control, zero trust and governance.
12 min readAgentic Finetuning: Training a Knowledge Wiki Instead of Model Weights
Run the ML training loop on a wiki of learnings mined from company documents: benchmarks built from raw data, secret questions, and a certificate with honest limits.
9 min readAgentic Engineering and the Outer Loop: Where Antigravity and Agents CLI Fit
Specs, evals and guardrails define agentic engineering. A look at Google's Agents CLI, its GCP production layer, and how Antigravity differs from other coding tools.
8 min readWhat MCP's Stateless Core and Managed Auth Mean for Integration Teams
Linux Foundation governance, stateless HTTP transport and identity-provider-managed authorization make MCP viable for enterprise integration. Here is what changed.
7 min readBefore an AI Agent Gets Production Authority: Contracts, Claim Graphs, Shadow Runs and Canaries
An evidence-first release process for AI agents: scoped contracts, stateful scenarios, fault injection, uncertainty bounds, staged canaries and machine-enforced promotion.
23 min readChoosing an AI Agent Development Partner: Questions Beyond the Demo
How to pick an AI agent development company in 2026: workflow scoping, integrations, permissions, failure tests, deployment visibility and who owns it later.
8 min readKeeping a Vector Store Honest: Lifecycle, Tenancy, Metadata, Hybrid Search
Running a vector store for RAG in production: document lifecycle, embedding refresh, tenant filters, hybrid search for exact IDs, metadata and tracing.
9 min read
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