Agentic Skill Decay: Why Your AI Agents Get Worse Over Time
Agentic AI systems drift in capability long before they fail outright. This post breaks down the mechanisms, the production signals, and the engineering practices that keep agents sharp.
Ai Agents
The Complete AI Agent Architecture: From Perception to Action
A field guide to designing production AI agents — from perception and reasoning loops to memory, tool routing, and the orchestration patterns that keep them reliable at scale.
Your First 7 Days with Grok Bot: A Production-Ready Field Guide
Seven days of practical lessons from onboarding Grok Bot into a real engineering workflow, from API setup to evaluation loops.
Building a pi-agent for Long-Horizon Tasks: Patterns That Actually Work
Pi-agents for long-horizon tasks need more than a clever prompt. This post covers the architecture, memory patterns, and failure modes that decide whether the agent finishes the job or burns budget on a loop.
Architecting Agentic RAG Systems From Vector Databases to Autonomous Knowledge Retrieval Workflows
Table of Contents Introduction Fundamentals of Retrieval‑Augmented Generation (RAG) Why RAG Matters Today Core Components Overview Vector Databases: The Retrieval Backbone Embedding Spaces and Similarity Search Choosing a Vector Store Schema Design for Agentic Workflows Agentic Architecture: From Stateless Retrieval to Autonomous Agents Defining “Agentic” in the RAG Context Agent Loop Anatomy Prompt Engineering for Agent Decisions Building the Knowledge Retrieval Workflow Ingestion Pipelines Chunking Strategies and Metadata Enrichment Dynamic Retrieval with Re‑Ranking Orchestrating Autonomous Retrieval with Tools & Frameworks LangChain, LlamaIndex, and CrewAI Overview Workflow Orchestration via Temporal.io or Airflow Example: End‑to‑End Agentic RAG Pipeline (Python) Evaluation, Monitoring, and Guardrails Metrics for Retrieval Quality LLM Hallucination Detection Safety and Compliance Considerations Real‑World Use Cases Enterprise Knowledge Bases Legal & Compliance Assistants Scientific Literature Review Agents Conclusion Resources Introduction Retrieval‑Augmented Generation (RAG) has emerged as the most practical way to combine the expressive power of large language models (LLMs) with up‑to‑date, factual knowledge. While the classic RAG loop (embed‑query → retrieve → generate) works well for static, single‑turn interactions, modern enterprise applications demand agentic behavior: the system must decide what to retrieve, when to retrieve additional context, how to synthesize multiple pieces of evidence, and when to ask follow‑up questions to the user or external services. ...