Insights & Research
AI & Automation
Agentic Systems · LLMs · MLOps · Enterprise AI
Deep-dives into applied artificial intelligence, multi-agent architectures, large language model strategy, and production-grade AI deployment from the MNB Technology Research Team.
Shadow AI: The Tool Sprawl Nobody Approved — MNB Technology
Employees are pasting company data into unapproved AI tools faster than security teams can write policy. Here is a workable response, not a ban.
Read article →Why Your RAG Pipeline Is Confidently Wrong and How to Catch It — MNB Technology
Retrieval-augmented generation does not stop hallucination; it changes its shape. A practical approach to catching confidently wrong, well-grounded-looking answers.
Read article →Prompt Injection Is a Supply Chain Problem, Not a Prompting Problem — MNB Technology
Prompt injection is not fixed by better system prompts. It is a trust-boundary and supply-chain problem, and needs the same controls as any other one.
Read article →The Permission Model Agentic AI Actually Needs — MNB Technology
Bolting RBAC onto an agent after the fact does not work. Agentic AI needs a permission model designed around tasks and tools, not users and roles.
Read article →MCP and the New Trust Boundary Between Agents and Tools — MNB Technology
Model Context Protocol standardises how agents call tools, which also standardises a new trust boundary. What enterprises need to govern before adopting it.
Read article →Designing Human-Approval Checkpoints That Don't Kill Velocity — MNB Technology
Badly designed human-in-the-loop checkpoints turn into rubber-stamping or bottlenecks. Here is how to design approval gates people actually engage with.
Read article →The Hallucination Budget: Treating Model Error Like an SLO — MNB Technology
You cannot eliminate model hallucination. You can budget for it the way SRE teams budget for error, which changes how you design around it entirely.
Read article →Evaluating AI Systems Like You'd Evaluate a Junior Hire, Not a Feature — MNB Technology
Feature-launch evaluation misses how AI systems actually fail in production. A better mental model: ongoing performance management, not a one-time QA gate.
Read article →Why Most Enterprise AI Pilots Never Reach Production — MNB Technology
Most enterprise AI pilots succeed on their own terms and still never ship. The reason is rarely the model. Here is what actually blocks production.
Read article →The Build-Your-Own-Model Decision Most Companies Get Wrong — MNB Technology
Fine-tuning or training a custom model is rarely the right first move. Here is the decision framework before committing engineering effort to it.
Read article →The AI Investment Layoffs Nobody Is Honest About — MNB Technology
Workforce cuts announced alongside AI investment are rarely simple substitution. Here is the honest version of what is actually being traded off.
Read article →Agents vs Humans: The future of workforce — MNB Tech
Enterprise agents will not replace humans; they will restructure work. We outline the architecture, controls and roadmap to deploy safe, productive AI workcells.
Read article →Agent Orchestration Patterns That Actually Hold Up in Production — MNB Technology
Multi-agent demos look impressive and collapse under production load. These are the orchestration patterns that survive past the prototype stage.
Read article →SpaceX OpenAI and Anthropic IPO: Is this Good or Bad for us? — MNB Tech
SpaceX, OpenAI and Anthropic eye 2026 IPOs. We unpack the $3.7T wave, technical implications for enterprise AI/space stacks, governance, and next moves.
Read article →Open AI vs Anthropic: Who is winning the race? — MNB Tech
OpenAI vs Anthropic: we dissect capability, cost, control and compliance—and map a pragmatic, multi-model enterprise architecture that wins in 2026.
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