Practical guide to deploying AI agents in production: architecture, testing, security and agent orchestration patterns for safe, scalable agentic workflows.

Compare GPT-4o, Claude 3, Gemini, Llama, Mistral and Falcon in one practical guide — capabilities, context windows, costs, safety, and deployment advice for CTOs & product teams.

  Executive summary AI is no longer an experimental add-on — it is a multiplier that changes how we cost, staff, and architect software projects. For CTOs and product managers, the primary questions are practical: How does AI alter total cost of ownership (TCO) over a multi-year horizon? How much does it shrink time-to-market? When […]

Organizations are embedding AI into products, internal tools, and decision chains at an accelerating pace. That speed brings real user value — automation, personalization, insight — and real operational, legal and ethical risk. Privacy breaches, hallucinated outputs that mislead users, intellectual property (IP) leakage, and inconsistent internal governance are now recurring headlines.

Over the next three years (2026–2028), the software development landscape will be reshaped by three converging forces: increasingly capable AI (LLMs and domain models), standardized composability of AI services, and the practical maturation of edge/on-device inference. Together they will automate routine development tasks, create new building blocks for app composition, and move latency- and privacy-sensitive […]

How to go from prototype to production with AI-origin code: policies to adopt, verification and testing steps to enforce, compliance touchpoints to check, technical guardrails