Production AI

Production Agents Need Workflow Graphs

The production abstraction for AI is not a “smarter agent loop.” It is a stateful execution graph: a system organized around explicit state transitions, dependency-aware scheduling, versioned plans, deterministic control boundaries, and carefully managed side effects. Intelligence alone is insufficient. Production reliability emerges from how execution, authority, recovery, and state mutation are structured across the graph.

The Runtime Behind Production AI

A layered framework for scaling production AI systems begins with the SLA: latency, throughput, reliability, cost per resolved task, fallback behavior, and quality targets. Those requirements drive the architecture of the runtime — spanning the edge gateway, safety and governance, orchestration and routing, inference serving, compute scheduling, context and state management, model lifecycle operations, and observability.

State Is the Hard Part of Production Agents

As AI agents move from short-lived chat interactions to long-running autonomous systems, the hardest engineering problems are no longer about prompts or model quality. They are about state management, replay safety, memory hierarchy, checkpointing, and transactional execution. Production agents need a cache-aware, transactional runtime. Agent state should not be a probabilistic byproduct of a chat log; it should be a deterministic projection of validated events.

Automating the Prompt Production Line

In production LLM systems, a prompt is no longer just a string written by a human. It is a deployable artifact. This post explains how automated prompt optimization actually works: build eval sets, collect optimization signals, generate candidates, and evaluate changes in stages. Prompts become versioned, testable artifacts with eval gates, canary rollouts, observability, and rollback.

Design Agents Around Workflows, Not Chat Turns

Chat is a useful interface, but it becomes a weak system design primitive once agents are expected to complete real work. A reliable agent should advance a process, not merely generate text. That requires routing simple requests to deterministic paths, using retrieval when grounding is needed, reserving reasoning for ambiguous tasks, and separating planning from execution. For repeatable workflows, LLMs can generate structured plans while deterministic engines handle tool calls, retries, and state transitions. Production agents should be designed around explicit, inspectable, and evaluable workflow state—not reconstructed from chat history every time.

Routing Before Reasoning

Production agents should not send every request to the most expensive reasoning path. As reasoning models become more capable, they also introduce new production risks: higher latency, unpredictable cost, KV-cache pressure, and unnecessary “overthinking” for simple requests. Before invoking deep inference, tool use, or multi-step planning, a production agent should first decide which path is actually needed. Production agents are control systems. The real engineering value is not only in the model, but in the controller that decides when to reason, when to execute, and when to ask for human approval.

The Production Agent Stack

A reliable agent is not just an LLM connected to tools. A production agent stack is a system of layered responsibilities. The runtime owns execution state and governs workflow progression. The planner proposes next steps, but proposals are not execution. Memory provides contextual recall without serving as the source of truth. Agent interoperability enables structured delegation, while tools expose external capabilities through standardized protocols such as MCP. Validation transforms probabilistic model outputs into structured, policy-constrained proposals that can safely enter the execution pipeline. Execution itself occurs inside isolated runtime environments where side effects can be controlled, audited, recovered, or rolled back.

Search Is Becoming Agent Infrastructure

Search is no longer just a user-facing answer interface. In production agent systems, it is becoming the context acquisition layer of the agent runtime. Traditional search returned ranked documents and left the user to interpret results. Early RAG systems followed a similar pattern: retrieve evidence, inject it into the prompt, and generate a response. But agents use search differently. They invoke search as an internal workflow step to clarify intent, retrieve evidence, choose tools, verify state, inspect logs, and recover from failures.

Demystifying Agentic Search Engines

Agentic search engines—such as Google AI Mode, Perplexity, Bing Copilot, ChatGPT Search no longer means “type keywords, get ten blue links.” AI Search experience capable of understanding tasks, planning queries, calling tools, and synthesizing results and deliver a conversational response with inline citations, minimizing user effort. In this post, I’ll walk through the stack from bottom to top, how it crawls and indexes pages, how it retrieves and ranks information, and how recent features like RAG and Agentic search build upon these foundations.

工程实验中的假设检验