Tagged AI agents · show all articles
Every era of AI agent architecture answers one question differently: what decides the next action. A 1971 planner, a learned reward signal, a prompted reasoning loop, a delegated sub-agent, and a shipped SDK are five dated, sourced answers.
A single loop, a plan handed to an executor, a supervisor delegating to workers, or a graph of nodes and gated edges — four documented wiring patterns for the same underlying control problem, each with a stated price.
An agent architecture is five engineering decisions wired into one harness — permission scoping, state persistence, checkpoint gates, retries, and instrumentation — each documented by a real SDK, each with a demonstrated production failure mode.
Two questions decide how agent architecture matures as an engineering discipline by 2035: pattern convergence or fragmentation, and certified reliability or empirical best-effort. Four scenarios, each with a falsifier.
Claude Code is not a chatbot with a code icon. It is an agentic harness: a loop that calls a model, executes real shell and filesystem operations behind a permission gate, and manages a shrinking context budget one turn at a time.
Crossing whether verification capacity keeps pace with code generation against whether permission and trust models converge on shared standards yields four falsifiable futures for Claude Code and agentic coding tools generally.
Two axes decide what retrieval-augmented generation becomes by 2035: whether evidence-gathering stays a separate, inspectable step, and whether what it returns can be verified. Crossing them gives four futures, and current evidence does not yet pick one.
An agent can do exactly what its tool interfaces let it propose, no more. The schema, the granularity, the retry semantics and the error surface are not plumbing around the intelligence — they are the boundary of it.
Codex, Claude Code, RAG, and MCP make more sense when treated as components in a measured feedback system rather than as personalities with tools.
OpenAI, Anthropic, and Google DeepMind are converging on the same bet: the next phase of AI competition will be won by whoever builds the most reliable autonomous agents, not the biggest chatbot.