AI Agents
Agentic AI, tool use, MCP, and automating real workflows safely.
9 articles
Human oversight in agent workflows: a practical playbook
As AI agents take on multi-step, consequential work inside real business processes, the question of when and how humans intervene has become a design problem, not a policy one. This playbook gives you a concrete sequence for building oversight into agent workflows before something expensive goes wrong.
Aug 13, 2026Human oversight in agent workflows: what it actually means to stay in control
As AI agents take on multi-step tasks with real consequences, the question of where humans intervene has become a design problem, not a policy slogan. This article unpacks the mechanics of oversight in agent workflows and explains when to tighten or loosen human control.
Aug 3, 2026Where AI agents help and where they break: lessons from Klarna
Klarna ran one of the most cited enterprise deployments of AI agents in financial services, and the results were genuinely mixed. Here is what actually happened, what the numbers mean, and what any organization should take from it before committing to agent-based automation.
Aug 1, 2026How JPMorgan Chase built human oversight into its AI agent workflows
JPMorgan Chase deployed AI agents across legal review and trading operations, then discovered that automation without structured human checkpoints created compliance exposure it hadn't anticipated. The decisions they made to redesign those workflows offer a concrete template for any organization running agents at scale.
Jul 28, 2026The Model Context Protocol: how AI actually connects to the world outside its context window
Most AI assistants are islands. The Model Context Protocol is the specification that turns them into networked systems, and understanding how it works changes what you can realistically build or demand from AI in your organisation.
Jul 25, 2026Human oversight in AI agent workflows: what it actually means to stay in control
As AI agents take on multi-step tasks autonomously, the question of when and how humans intervene has become one of the most consequential design decisions in enterprise AI. This article breaks down the mechanics of oversight in agentic systems and the real tradeoffs involved.
Jul 16, 2026AI agents in the enterprise: what breaks before it works
AI agents are moving from demo to deployment across industries, and the gap between the two is where most organizations stumble. Understanding what actually fails, and why, is more useful than another architecture diagram.
Jul 9, 2026AI agents in the enterprise: what actually breaks and how to fix it
AI agents are moving from demo to deployment across major organizations, and the gap between promised efficiency and real-world performance is proving instructive. Understanding where agent workflows fail is now more operationally valuable than understanding how they work in theory.
Jul 2, 2026AI agents at work: what actually breaks and how to fix it before it costs you
AI agents are moving from demos to production, and the gap between the two is where most organizations lose time and credibility. Understanding where these systems fail in practice is more valuable right now than understanding how they work in theory.