Blog Archive
Observability for Agents: Tracing Tool Calls and Reasoning
An agent is a loop, not a single API call. Instrument it with OpenTelemetry AGENT/LLM/TOOL spans and a shared trace_id so you can attribute bad reasoning, slow dependencies, and tool failures.
RAG Evaluation: Measuring Retrieval Quality Objectively
A RAG pipeline fails in two halves: retrieval and generation. Build a transparent Python eval harness computing hit_rate, MRR, context precision/recall, and faithfulness to know exactly which half to fix.
Building Guardrails for LLM Outputs
Turn "the model should not do that" into a named, auditable gate. Build a deterministic validation layer with Guardrails AI and Gemini structured outputs — schema plus semantic checks, with bounded reask.
Beyond AI: An Executive Brief for C-Level Leaders
AI changes every month, but the principles of designing work change every ten years. An executive brief on how to manage AI — warning signs, the resource mindset, five strategies, and the role each C-Level leader must own.
Gemini 3 Robotics: Bridging the Gap Between Reasoning and Physical Motion
Reasoning and motion used to live in different worlds. Learn the two-brain robotics pattern: an embodied reasoning model that plans and coordinates, and a vision-language-action model that moves.
Zero-Knowledge Agents: Processing Private Data with Gemini 3's Encrypted Reasoning
When agents touch private data, you need two promises: the provider computes without seeing it, and you can verify the model really ran. Learn ZK proofs, FHE, TEEs, and the collaborative SLM/LLM split.
The "Long-Horizon" Task: Keeping Gemini 3 Focused Over Months-Long Projects
Long-horizon tasks outlast a single context window. Learn to beat context rot with execution-state memory, agent-managed context tools, file-centric state, and proactive memory intervention.
The "Continuous-Learning" Agent: Updating Gemini 3 Knowledge via Live Feeds
An LLMs knowledge freezes at training time. Learn the continuous-learning agent pattern: frozen base + live vector store, temporal scoring, EWC-guarded micro-adaptation, and indexes that learn from validated queries.
Building a Meta-Agent: The Gemini 3 Orchestrator of Orchestrators
A meta-agent is a higher-order agent that operates on other agents. Learn the orchestrator-of-orchestrators pattern: hierarchical planning, sub-agent split, lazy tool discovery, and Git-like execution traces.
Human-Agent Collaboration: Designing UIs for Gemini 3 "Co-Pilot" Modes
A copilot UI is not a chat UI with autonomy bolted on. Learn the seven agent UI patterns, autonomy as a design decision, approval gates that dont train users to click through, and the AG-UI/A2UI protocol stack.
Hyper-Personalized SLMs: Local Fine-Tuning of Gemini 3 Nano Models
A model that runs on-device still has to be trained somewhere. Learn to fine-tune Nano SLMs locally with LoRA, QLoRA, MeSP, and GradCut — without ever shipping user data to the cloud.
Wasm-Powered Agents: Running Secure Gemini 3 Nano Logic in the Browser
Ship an entire AI agent as a single HTML file. Learn the Wasm-powered agent pattern: Pyodide vs WebLLM, sandboxing agent-generated code in WASM kernels, and wiring MCP tools from a browser tab.