How Agent Development Toolchains Evolved: From Basic Frameworks to Model‑Centric AI

Although the underlying agent application architecture remains relatively stable, the surrounding development toolchain advances rapidly, driven by the need for more reliable and deterministic outputs from large language models.

Stage 1: Basic Development Frameworks

At the end of 2022, ChatGPT revealed the potential of large language models, but early LLMs were isolated and could not mobilize developers. The first wave of agent frameworks—such as LangChain and LlamaIndex —introduced modular abstractions (model communication, ChatClient, Prompt, output formatting, Embedding) to simplify chatbot creation, context handling, and model invocation.

In 2024, Spring AI (Aliyun) added high‑level AI API abstractions and cloud‑native integration, enabling Java developers to build AI applications quickly. It is positioned as a bridge between Spring and the AgentScope ecosystem, with a Java version planned for release in November 2024.

Stage 2: Collaboration & Tools

Early frameworks were not friendly to non‑programmers, limiting team collaboration. Between 2023‑2024, low‑code/zero‑code platforms such as Dify and n8n entered production environments, offering workflow editors, conditional branches, and even natural‑language generation of simple front‑end pages, thereby improving cooperation between domain experts and developers.

On the tooling side, OpenAI launched Function Calling in June 2023, and Anthropic released the MCP protocol in November 2024, enabling cross‑model tool interoperability and energizing the developer ecosystem.

Stage 3: Reinforcement Learning

Model intelligence alone cannot reliably interact with the physical world; static prompt engineering proved insufficient for consistent output. The community turned to reinforcement learning (RL) to make context engineering dynamic. Key RL‑driven improvements include:

RAG retrieval ranking: RL optimizes document re‑ranking to keep context semantically aligned with tasks, reducing noise.

Multi‑turn conversation memory: RL learns when to retain or forget memory, preserving coherence over long interactions.

Tool invocation: RL decides the timing and parameter construction for tool calls, increasing effectiveness and correctness.

Practices such as Jina.AI’s search stack (Embeddings, Reranker, Reader) and Alibaba Cloud API Gateway’s RL‑based tool selection and semantic retrieval have demonstrated measurable gains: up to 6 % higher tool‑selection accuracy, up to 7× faster response time for large tool sets, and 4‑6× token‑cost reduction.

Stage 4: Model‑Centric Architecture

By late 2025, major model providers began embedding agent capabilities directly in the model layer. OpenAI released AgentKit and Apps SDK , while Anthropic introduced Claude Skills . These solutions host memory, tool registries, and external‑application logic on the model side, dramatically lowering development barriers.

Claude Skills, for example, let the model load and manage “skills” (e.g., Python scripts) that can invoke APIs without an external MCP layer. Users supply a skill.md file containing code, design specs, or assets, and the model incorporates it as contextual knowledge, improving output consistency especially in collaborative scenarios.

Stable Cloud‑Native Infrastructure Layers

AI Gateway : aggregates models and tools, provides intelligent scheduling, authentication, load balancing, rate limiting, and request tracing.

Runtime : supplies compute resources, task scheduling, state management, security isolation, timeout handling, and concurrency tracking; works for both private deployments and multi‑model cloud orchestration.

Observability : end‑to‑end logging and tracing of model, tool, RAG, and memory components; exposes throughput, error rates, and resource usage to ensure stable, secure, and efficient operation.

Security : enforces identity verification, access control, data masking, and privilege protection, especially critical in multi‑tenant, multi‑model environments.

The rapid iteration of the upper‑layer toolchain boosts output reliability, while the lower‑layer gateway, runtime, observability, and security modules keep applications stable, economical, and safe. This “fast‑changing top, steady bottom” structure enables the AI application ecosystem to innovate quickly without descending into systemic chaos.