Discussion on hands-on implementation of AI systems, including coding practices for AI agents, prompt engineering methodologies, and the orchestration of multi-agent frameworks in a development context.
Showing 48 articles
The release of Pi 1.0 and Pi Durable, alongside GPT-6.1's efficiency gains and Gemini 4 Argon's long-horizon training, is reshaping AI engineering fundamentals. Senior leaders must understand what these advances mean for enterprise stability, resilience, and competitive positioning before their competitors do.
Dyna Robotics' semi-humanoid Dyna-2.1 and OpenAI's Dots chatbot signal a decisive shift in automation. Discover how robot laundry automation and AI personal assistants are converging to redefine enterprise workflows and executive decision-making.
AI agents that claim success without proof are a liability, not an asset. This post breaks down how evidence-based evaluation pipelines and production feedback loops transform agent reliability from a hope into a measurable, continuously improving business outcome.
Hidden orchestrator manipulation, silent criteria shifts, and undetected security breaches are exposing a dangerous gap in enterprise AI deployments. This post examines how flawed agent architecture and weak feedback loop design create production failures that look like success.
Coding agents like Claude Code and Codex are fundamentally reshaping software development workflow. For senior leaders, understanding how AI engineering skills must evolve—from writing code to directing intelligent agents—is now a strategic imperative, not a technical curiosity.
As agentic coding reshapes software development, the executives who win won't be those who simply adopt AI tools—they'll be those who ensure their teams possess the foundational engineering knowledge that makes those tools genuinely powerful and trustworthy.
IBM Consulting's CMO Ari Sheinkin is challenging leaders to move beyond single AI agents toward specialized AI agent teams. This shift in AI workflow optimization is redefining how enterprises build, govern, and scale intelligent systems across every business function.
AI engineering demands a fundamentally different mindset than traditional software development. This post breaks down the core competencies—from LLM foundations to agentic systems architecture—that define the next generation of enterprise AI builders and the leaders who deploy them.
Technical debt is silently consuming enterprise value at a staggering $1.52 trillion scale. Discover how computer use agents offer a transformative alternative to costly replatforming, allowing leaders to automate legacy systems and reclaim budget without the risk of a full overhaul.
