AI Memory: Rethink it as an Index, Not a Database
Why conversational systems require pointers over payloads
The Illusion of the Action Item: How AI Agents Fail to Understand the Social Negotiations of Real Work
There is a significant gap between the way automated systems handle task assignment and the reality of human interaction. While we anticipate that proactive AI can accurately identify "action items" from our discussions to oversee workflows, traditional software often fails to account for the nuances of Social Communication.
Your AI Agent Telemetry is More Valuable Than You Think: Execution Trajectories and the Dawn of CL/CD
Engineering teams mostly ignore their LLM and agent telemetry by treating it like traditional software logs: a depreciating asset to be discarded. In the world of legacy software, logging serves primarily as a cost center. We record stack traces and database timeouts to squash bugs, then rely on short retention
The End of the Static AI Harness? Why Agents Generating Agents on the Fly Changes Everything
The development of reliable AI agent systems has closely mirrored traditional software engineering over the past two years. Carrying out complex, long-term tasks, such as thorough codebase audits or large-scale platform migrations, necessitated the creation of dedicated pipelines. Developers were tasked with establishing the rules, mapping the workflows,
Reusable Knowledge vs. Operational Memory
The missing distinction in building AI agents that can actually follow through
LLMs Are Now Faithful, But They Can Be Annoying Due to Overthinking
Designing Delightful Agent Experiences Without Breaking Safety Boundaries
From Task Machine to Decision Partner: Building AI Agents Around Human Judgment
From Task Machine to Decision Partner: Building AI Agents Around Human Judgment
Knowing When To Act: Temporal Interpretation - The Underrated But Critical Ingredient In Proactive AI Agents
Knowing When To Act: The Underrated But Critical Ingredient In Proactive AI Agents
The Love Child of OpenClaw and Claude Cowork: The Rise of Proactive AI Agents
Most AI agents today are evolving along two distinct paths. The first path is the always-on personal agent. This type of agent promises continuity: it knows the user over time, remains accessible across different channels, and can interact with tools, automations, and connected devices. Its focus is on being
The Stateful Long-Range Dependency Problem in AI Agents
An agentic approach to building context to achieve reliable conversational intelligence. Conversational AI agents often struggle to reason smoothly across long-horizon conversations because their contexts are disconnected. For instance, if someone says, "I'm tired," today, the agent treats it as an isolated comment without considering