Blueprints and engineering strategies for scaling enterprise AI from pilot to production.
Enterprises run AI agents on persistent API keys, tokens, and service accounts, and most security teams cannot say where all of them live. Here is where agent credentials actually go missing in 2026.
Most performance test suites still simulate one request and one response. Production AI agents fail through rate limits, retries, and tool-call fan-out that most QA pipelines never test for before launch.
71% of enterprise agentic AI deployments have no formal access control policy governing what agents can read, write, or execute. When an agent with production credentials makes an unintended action, the investigation takes 3.8x longer than a human-triggered incident. Here is what to lock down before deployment.
Most GCCs now have a dozen or more AI vendors on the books, one per delivery pod, with no shared criteria for who gets selected or reviewed. Nobody owns the full list, so nobody owns the risk sitting inside it either.
68% of LLM incidents have detectable signals beforehand. Here is what to monitor in production to debug AI quality issues before users complain.
Enterprise AI agent fleets have doubled since December 2025 while the humans reviewing their decisions have not. Here is what that oversight math actually looks like in 2026.
58% of LLM quality issues trace to retrieval failures. Here is the pre-launch RAG evaluation framework enterprise engineering teams skip.
Enterprise teams are replacing human review with LLM-as-judge models to score AI-generated code, test results, and agent outputs at production speed. Few of those judge models have ever been checked against a human-labeled baseline before they started deciding what ships.
Most Indian GCCs have stood up an AI Center of Excellence. Few can prove it can approve a budget, veto a vendor, or say no to a use case in 2026.
No 50-page proposals. We'll tell you which level fits your situation, what a realistic engagement looks like, and what it would cost — in one direct meeting.
Thomas leads 10decoders' AI engineering practice and sits in on the scoping call himself — so the person mapping your engagement is the one who has shipped it before. His teams build and deploy agents for mid-market healthcare and fintech companies, with enterprise grade build experience for clients like IBM, Dedalus and Harris Healthcare. He'll be straight with you about what's worth doing and what isn't.
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