Agent Memory Is Not a Database: Start with an Event Log
I break agent memory into writing, storage, retrieval, assembly, and forgetting, then outline a minimal implementation path that starts with a durable event log.
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I break agent memory into writing, storage, retrieval, assembly, and forgetting, then outline a minimal implementation path that starts with a durable event log.
A Skill teaches an agent how to work, a Tool lets it act, MCP connects it to external capabilities, and a Plugin packages the pieces for installation.
A source-based comparison of Pi, Codex, and OpenCode shows how a minimal coding harness leaves workflow policy and isolation to the user.
A commit-pinned reading of the Matt Pocock Skills repository shows how its invocation rules keep workflow direction with the human while the host retains tools and permissions.
After moving to GPT-5.6, I began choosing smaller agent workflows and paying more attention to the loop that decides what happens next.
Reusable skills are helping me give agents clearer working context, spend less time repeating process, and keep the important decisions reviewable.
How I moved a personal RAG chatbot to Cloud Run with PostgreSQL, session-aware retrieval, and Vertex AI.
I wrapped my multi-agent chatbot in Docker and FastAPI so I could use it through a simple browser interface.
I added safety checks, retrieval ranking, and response editing to make my personal assistant more reliable.
I built a personal assistant around my Obsidian notes to learn how retrieval, agents, and personal knowledge can work together.
I moved my AI newsroom from CrewAI to LangGraph to gain clearer state, workflow control, and more consistent output.
I built a small AI newsroom with CrewAI to collect, filter, and summarize the AI news I actually want to read.
A practical introduction to agent workflows, using a CrewAI newsroom example to explain roles, loops, and trade-offs.