workflow-design

The article emphasizes that in AI agents, the harness—comprising tools, context, controls, and workflows—is more critical than the model for achieving reliable, safe, and traceable outcomes.

The article explores practical security workflows for AI agents in environments that combine homegrown, commercial, and cloud-based systems.

The author shares lessons learned from building agent graphs, highlighting the pitfalls of parallelism in cyclic workflows and advocating for sequential dependent checks and mechanisms to challenge feedback for better efficiency.

This article introduces a meta-Skill designer called Dao-Skill, which helps users generate runnable and verifiable Agent Skills from chaotic requirements through six steps, more powerful than Codex's built-in Skill-creator.

The article suggests that a simpler AI agent architecture with one strong agent and one reviewer may be more effective than complex multi-agent setups, reducing coordination overhead and improving accuracy through independent verification.

The author shares insights on building AI agents for small businesses, emphasizing that the hardest part is selecting the right task to automate, not the agent itself. Key considerations include volume, input structure, cost of errors, and existence of manual processes.

The author shares that they stopped comparing AI models and focused on workflow design, leading to improved output. They argue that workflow has more leverage than model choice for most practical use cases.

Argues that the key skill for product managers in the AI era is loop engineering, not prompt engineering. Describes how to create reusable, self-improving loops for AI agents to maintain quality and avoid drift.

An analysis of why teams quietly abandon AI tools due to broken trust, arguing that the real problem is not model quality but the lack of trust architecture—designing workflows that clearly indicate when AI output is reliable and when it needs verification.

The author explains why they switched from single agents to chained workflows for AI tasks, citing improved reliability and easier debugging despite higher upfront complexity.

The article argues that giving AI agents access to data through MCP tools (like querying Jira) is not the same as having native structured context like code files. It emphasizes that true understanding requires more than just API access, analogous to having a library card versus having read the books.

The article argues that AI agent safety rules should be implemented as hard workflow constraints and permissions rather than relying solely on prompt instructions. It emphasizes the need for explicit checks, approvals, and logs for sensitive or irreversible actions.

The author argues that in enterprise AI agent development, operational reliability and stability are more critical than high autonomy, advocating for controlled intelligence over fully autonomous systems.

A technical analysis proposing that agent frameworks should distinguish between what a skill describes (persona, tool, workflow) and how it executes (stateless vs stateful), arguing this distinction is crucial for building robust real-world agent systems.