Agent

A philosophical boundary and an engineering practice for continuity in AI agents Start by Making “Identity” Small Enough to Engineer Agent amnesia first damages the cost of cooperation, not the illusion of personhood. A strong session does not guarantee that the next one will preserve the same judgment. Users repeat preferences, teams restate constraints, and the system struggles to explain where an earlier decision came from. Long-term trust rests on these unglamorous forms of continuity: what the agent remembers, what it forgets, why it changed, and who approved the change. ...

This project note is part of my attempt to understand open-source AI systems by building with them, reading their migrations, and writing down where the abstraction holds—and where it leaks. Project learning list The problem is not remembering more An LLM can carry a conversation while the relevant messages still fit inside its context window. That is short-term continuity, not memory in the human sense and not durable application state. When the session ends, the model does not naturally retain that a user prefers terse answers, changed jobs last month, or abandoned an earlier plan. ...

Why UFO matters Desktop automation has always had an awkward boundary. Scripts are fast and precise when an application exposes the right interface. GUI automation is broad, but a changed label, a delayed window, or a custom-drawn control can break an otherwise sensible workflow. A vision-language model can interpret what it sees, yet perception alone does not make an action reliable. Microsoft’s UFO project is interesting because its evolution follows that boundary instead of pretending it does not exist. ...

LangChain used to be introduced as a box of chains, memory classes, prompt templates, loaders, and integrations. That description is historically accurate and operationally unhelpful. As of July 2026, the useful way to understand LangChain 1.x is much narrower: Call a model directly when the task is one inference with a clear input and output. Use create_agent when a model must choose among tools in a conventional model–tool loop. Use LangGraph when your application has durable state, explicit branches, retries, parallel work, long-running steps, or human decisions that are part of the workflow. This is not merely a taxonomy. It is an engineering rule about where complexity should live. Every framework abstraction creates a second system beside your product: another execution model, another vocabulary, and another place for failure to hide. The abstraction earns its place only when it makes the real system easier to operate. ...

A framework is most dangerous not when it lacks features, but when its old tutorials still look plausible. Familiar code can cross a version boundary long before it produces an obvious error. This guide follows the current LangChain Python 1.x path: model calls, agents, tools, structured output, middleware, memory, RAG, SQL, Ollama, and LangSmith. It is deliberately different from a framework overview. The aim is to leave you with a compact application architecture, runnable examples, and a way to recognize code that belongs to the 0.x era. ...

This article began as notes from a large-language-model meetup in January 2024. Back then, the question was what a model could do. Two years later, the harder question is why anyone should trust the system around it. I have not polished the old notes into a story in which every early judgment was right. Mistakes are useful sediment. They remind us that a technical opinion is not a prophecy; it is a bet with an expiry date. Each section therefore keeps the 2024 observation and adds a 2026 reassessment: still true, changed, or wrong at the time. ...

Status note, verified July 31, 2026: this article originally explained how to install the 2023 stand-alone Auto-GPT agent. Those commands are obsolete. The official project now says that AutoGPT Classic is unsupported, its dependencies will not be updated, and it has known security issues. Treat Classic as a historical laboratory, not a production tool. For new work, use the maintained AutoGPT Platform or another actively maintained workflow system. In the spring of 2023, Auto-GPT made a compelling promise: describe an objective, give a model some tools, and watch it plan its own way forward. I installed it because the idea felt less like a chatbot feature and more like a change in the shape of software. ...