Agentic AI Operations
Deep technical guides on running LLMs, AI agents, and ML systems in production — pitfalls, scaling patterns, performance, governance, and the operational playbooks that separate pilots from enterprise deployments.
Common Pitfalls of Running LLMs in Production
Latency spikes, hallucination at scale, context window mismanagement, cost explosions, and the 12 failure modes that catch every enterprise LLM deployment off guard — with fixes for each.
Common Pitfalls of Enterprise AI Agent Deployments
Agents that loop, escalate incorrectly, contradict each other, or corrupt the system of record. The 10 structural failure modes of multi-agent systems and how to design against them.
Common Pitfalls of ML Algorithms at Enterprise Scale
Data leakage, distribution shift, feature store inconsistency, retraining debt, and the silent accuracy degradation patterns that destroy production ML models over time.
Scaling Agentic AI: Architecture, Infrastructure & Patterns
From 10 to 10,000 agent actions per minute — orchestration architecture, actor-model vs queue-based agents, stateless design, horizontal scaling, circuit breakers for LLM calls, and the distributed systems patterns that separate pilots from production.
Performance Optimization for LLMs and AI Agents in Production
Inference latency, token throughput, caching strategies, batch processing, model quantisation, and the performance engineering patterns that cut LLM operational cost by 40-70%.
Change Management for AI Agent Deployments in Enterprise Operations
Why 70% of AI agent deployments fail organisationally, not technically. The change management framework, stakeholder map, communication strategy, and role redesign playbook for successful transitions.
Version Control and Model Governance for Production AI
Model registry design, prompt versioning, dataset versioning, rollback protocols, A/B testing for agents, and the audit trail requirements that make enterprise AI governable and auditable.
MLOps Best Practices for Agentic AI Operations
The complete AgentOps lifecycle — from model selection and prompt engineering through deployment, monitoring, retraining, and sunset. The operational playbook for enterprises running AI agents at scale.