Blog

MLOps Community is joining the Linux Foundation as the official user group of the Agentic AI Foundation. The community continues, with more support behind the events, newsletter, podcast, and practitioner conversations.

Subtitle: It’s a feature of the architecture Summary: Hallucination in LLMs is not a data quality problem. It is not a training problem. It is not a problem you can solve with more [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedbac), better filtering, or a larger context window. **It is a structural property of what these systems are optimized to do.** I have held this position for months, and the reaction is predictable: researchers working on retrieval augmentation, fine-tuning pipelines, and alignment techniques would prefer a more optimistic framing. I understand why. What has been missing from this argument is geometry. Intuition about objectives and architecture is necessary but not sufficient. We need to open the model and look at what is actually happening inside when a system produces a confident wrong answer. Not at the logits. Not at the attention patterns. At the internal trajectory of the representation itself, layer by layer, from input to output. That is what the work I am presenting here did.

A deep dive into the practical limitations of agent protocols like MCP and A2A for low-level tasks, and why the "Linux philosophy" of using a raw command-line interface provides a more lightweight, composable alternative for local development, paving the way for an Agent OS.

The 5xP Framework is a practical strategy that uses five targeted Markdown files (Product, Platform, Process, Profile, and Principle) to seamlessly align AI coding assistants with your project's architecture and business goals. By defining strict context boundaries, this framework drastically reduces prompt bloat and prevents AI hallucinations, moving developers away from unstructured "vibe coding" and closer to reliable, spec-driven development.

This blog walks through how to build an AI agent that can meaningfully use a unified collection of multimodal assets like speaker bios, talk titles or descriptions, and eventually their PDF or video content, by pairing the right tools with the right memory design. It demonstrates how retrieval, parsing, and reasoning components must be engineered so the agent can navigate relationships, interpret metadata, and answer higher‑order questions with accuracy.  By grounding the workflow in a multimodal database like ApertureDB, the agent gains reliable access to structured context, enabling richer insights across any real‑world content collection.

The blog shows how to unify scattered multimodal assets, e.g., speaker bios, talk titles, videos, and PDFs. into a single, well‑structured memory layer.  It explains the metadata and schema decisions that let an agent answer richer, cross‑asset questions such as trending topics, influential speakers, or patterns across a full conference.  By grounding these relationships in a multimodal database like ApertureDB, the approach generalizes to any domain where organizations need AI to reason over diverse, real‑world collections of content.

AI coding platforms work best when you treat the AI as a junior engineer, not a replacement for your thinking. Break problems into small tasks, plan in Markdown before coding, and keep your context window lean - accuracy drops sharply past 50% capacity. Never debug in the same chat where you built the feature; the AI is biased by its own logic. For existing codebases, reference well-written code as examples. For new projects, define strict guardrails early - without them, the AI makes hundreds of arbitrary decisions that compound into a mess. The blog dives deep into all the patterns that work, the anti-patterns that silently kill your codebase, and strategies for both brownfield and greenfield projects - each illustrated with detailed diagrams. You stay the architect; the AI executes.

Chaigent combines Chainlit and Vertex AI to deliver a code-first, serverless AI agent platform that avoids costly per-seat licensing fees. It empowers developers to build highly customizable, enterprise-grade agents using a scalable pay-as-you-go architecture.