Analysis10 Jan 2026
The AI Landscape in 2025: What Actually Happened
A clear-eyed look at what mattered in 2025: the agent revolution, reasoning models, MCP, and the real economics of AI adoption. What it means for your 2026 strategy.
Actionable thinking on AI strategy, implementation, and what actually works in production. Written by the engineers who build these systems.
A clear-eyed look at what mattered in 2025: the agent revolution, reasoning models, MCP, and the real economics of AI adoption. What it means for your 2026 strategy.
AI coding tools have evolved from autocomplete to autonomous agents. How Claude Code, Copilot agent mode, and agentic workflows are reshaping how production software gets built.
Architecture patterns, cost management, and the pitfalls we see teams hit when connecting LLMs to production systems. From API setup through to scaling and monitoring.
A decision framework for the full spectrum, from pure off-the-shelf to fully custom. When to invest in proprietary models and when existing tools are the smarter commercial play.
How Retrieval-Augmented Generation works, the key design decisions around chunking and retrieval, where most implementations go wrong, and advanced techniques that actually help.
Establishing baselines, defining metrics by use case, calculating hard ROI, and accounting for strategic value. The measurement framework we use with our own clients.
The regulatory landscape, governance frameworks that actually work, addressing bias in production systems, and incident response. What you need before deploying AI at scale.
When off-the-shelf performance plateaus, should you invest in better prompts or fine-tune the model? A detailed cost-benefit comparison with decision criteria.
Beyond the hype: which agent architectures actually work in production. The ReAct pattern, tool use, multi-step reasoning, and the practical limits of today's autonomous systems.
Accuracy alone tells you almost nothing. Evaluation frameworks, LLM-as-judge approaches, human evaluation design, and how to build testing pipelines you can actually trust.
Edge AI architectures, model compression techniques, and the trade-offs between on-device and cloud processing. When local inference is a business requirement, not a nice-to-have.
You don't need an enterprise budget to benefit from AI. Practical, affordable entry points that deliver measurable value for small and mid-sized businesses.
How embeddings work, how to choose between Pinecone, Weaviate, Qdrant, and pgvector, and the implementation patterns that determine whether your RAG system actually performs.
AI projects have fundamentally different risk profiles than traditional software. How to handle uncertainty, set realistic expectations, and structure teams for success.
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