OpenAI’s Assistants API and custom agent frameworks represent two fundamentally different approaches to building AI agent applications. The Assistants API gives you a managed, hosted agent infrastructure with built-in tools, persistent threads, and file handling — you configure rather than build. Custom agents give you full architectural control at the cost of building and maintaining … Read more
Agent orchestration patterns are the architectural templates that determine how multiple agents (or a single agent across multiple steps) coordinate to accomplish a task. Just as software engineering has design patterns for recurring problems — factory, observer, singleton — agent systems have patterns for recurring coordination challenges: how to divide work, how to manage dependencies, … Read more
Claude’s tool use API lets you give the model a set of callable functions — web search, database queries, code execution, API calls — and have it decide when to call them, with what arguments, and how to use the results to complete a task. It is the foundational mechanism for building agents with Anthropic’s … Read more
Agno (formerly Phidata) is an agent framework designed for building multi-modal, multi-agent applications with a strong emphasis on built-in memory, knowledge, and tool integrations. It positions itself as a full-stack agent platform rather than a minimal orchestration layer — providing pre-built integrations for databases, vector stores, file systems, and external APIs alongside the agent loop … Read more
A coding agent and a standard LLM used for coding are not the same thing, even when the underlying model is identical. The distinction is architectural: a standard LLM generates code in a single prompt-response cycle and stops. A coding agent operates in a loop — it writes code, executes it, reads the output, decides … Read more
LangMem is LangChain’s dedicated memory library for AI agents, released in early 2025. It provides a structured system for managing the four types of agent memory — in-context working memory, episodic memory of past interactions, semantic memory of facts about the user and domain, and procedural memory of learned workflows — with explicit extraction, storage, … Read more
Pydantic AI is an agent framework built by the team behind Pydantic, the Python data validation library used by FastAPI and most of the modern Python ecosystem. Released in late 2024, it takes a different design philosophy from LangGraph, CrewAI, and AutoGen: it prioritises type safety, structured outputs, and native integration with Python’s type system … Read more
Evaluating AI agents is harder than evaluating standard language models, and teams that do not invest in it early pay for it later through production failures they cannot diagnose. The challenge is that agent quality is multidimensional: an agent can produce a correct final answer via a broken reasoning path, or produce a wrong answer … Read more
Memory is what separates a useful AI agent from an expensive autocomplete tool. Without memory, an agent forgets every conversation the moment it ends, cannot learn from past interactions, and cannot build understanding of a user or task across sessions. With the right memory architecture, an agent accumulates context that makes it progressively more useful … Read more
Adding web browsing to an AI agent lets it retrieve current information, navigate websites, fill forms, and extract data from live pages — capabilities that static language model knowledge cannot provide. A browsing-capable agent can check today’s news, look up a product price, read a documentation page, or scrape a table of data in real … Read more
