Insights
The WeBuild-AI Perspective on Context Engineering
Context Engineering promises dramatically better AI outcomes, yet the reality involves substantial trade-offs in token economics, latency, and MCP infrastructure investment that determine what's actually feasible at enterprise scale.
Why Small Language Models Are the Key to Agent Independence
Open source small language models offer organisations a strategic path to building AI agents that avoid vendor lock-in, enable explainability for regulated industries and provide operational independence from the three dominant LLM providers.
Why Your Organisation Needs Agent Lifecycle Management
Explore why organisations should adopt full lifecycle management for AI agents for monitoring, governing, versioning and maintaining in business systems.
How We Harness AI to 10X our Software Engineering Teams
How does WeBuild‑AI integrate AI deeply into development processes, freeing engineers from repetitive work and accelerating delivery while preserving code quality?
Joining the 5% Inner Circle: Moving Beyond the AI Failure Narrative
Discussing what organisations must do to join the small group that succeeds in AI adoption - only 5%, according to MIT research from 2025.
What Metrics Matter for AI Agent Reliability and Performance
What are the key metrics and measurement strategies that organisations should monitor to ensure their AI agents behave reliably, safely, and usefully?
Why Prefect is A Perfect Pick for AI Agent Monitoring
Exploring how Prefect (a workflow orchestration tool) fits naturally into AI agent monitoring and enables tracing, alerting and observability of agent operations.
Delivering Differentiated AI-Enabled Products: Practical Field Lessons for the Enterprise
From field experience, WeBuild-AI shares lessons on how enterprises can build AI‑enabled products that are truly differentiated and scalable.
Introducing Tim Doughty Our New Lead Product Owner
An introduction post to Tim Doughty, Lead Product Owner at WeBuild-AI, sharing his background, role and vision in leading product strategy.
WeBuild-AI Secures Multi-Million Pound Strategic Investment to Accelerate Enterprise AI Transformation
WeBuild‑AI will scale its mission of transforming enterprise AI adoption through product, AI agents and governance frameworks, thanks to new investment.
Will MCP Make RAG Obsolete?
We examine the relationship between Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG), asking if MCP’s capabilities will remove the role or shift the relevance of RAG in enterprise systems.
Practical Lessons from Deploying MCP in the Enterprise
Practical learnings from real enterprise deployments of MCP: architecture decisions, challenges, tradeoffs and guidelines for adoption at scale.
Protecting Enterprise Data in the MCP Era
Covering the data governance, security and privacy challenges that arise when connecting AI agents to enterprise data via Model Context Protocol (MCP), as well as how to mitigate risks.
How MCP Transform Enterprise Intelligence
How MCP enables AI systems to make insights more actionable, integrated and contextually aware, based on relevant enterprise data.
What is Model Context Protocol and Why Should You Care?
Model Context Protocol (MCP) lets AI systems securely interface with enterprise data, breaking silos and embedding context into AI outputs. Read on to find out more.