The Agentic Web: How AI Agents Are Rewriting the Rules of Software

In 2023, the world was dazzled by chatbots that could answer questions. In 2024, reasoning models started showing chain-of-thought that mimicked human deliberation. But 2025 is quietly becoming the year of something far more consequential: the agentic web — a paradigm where AI systems don't just respond to prompts, they act, decide, and delegate across a growing ecosystem of tools, APIs, and services.

This isn't hyperbole. The evidence is accumulating at every layer of the stack.

From Tool Use to Tool Ecosystems

For years, "AI with tools" meant a language model that could call a single function — often a glorified calculator. The breakthrough that changed everything was giving models not just tools, but agency over which tools to use, in what order, and with what parameters — dynamically, at runtime, based on context.

The technical enabler was multi-turn reasoning combined with systematic tool-calling abstractions. When a model like Claude, GPT-4o, or their successors can receive a high-level goal and autonomously decompose it into sub-tasks — each invoking different tools, chaining results, handling errors, and looping until the objective is met — you've crossed a threshold. The model becomes a programmable agent, not just a responsive system.

Today, agents can:

• Browse the web, extract structured data, and synthesize findings across dozens of pages
• Write and execute code, run tests, and iterate until a bug is fixed
• Manage files, schedule events, send emails, and control IoT devices
• Orchestrate other AI agents in hierarchical workflows

The pattern is consistent: the bottleneck is no longer the model's intelligence, but the ** breadth and reliability of its tool ecosystem**.

The Rise of Delegation Chains

One of the most underappreciated dynamics in today's AI landscape is delegation chaining — the practice of one AI agent spawning or routing tasks to other specialized agents. What began as a curiosity (a "manager agent" that delegates to a "coder agent" and a "reviewer agent") has matured into a fundamental architectural primitive.

Consider what this means for software development. A product manager describes a feature in natural language. A "spec agent" converts that into a technical specification. A "code agent" implements the specification. A "test agent" validates correctness. A "docs agent" writes the documentation. Each step may involve further sub-delegation. The entire pipeline runs autonomously, with human oversight reserved for boundary cases and sign-off.

This isn't science fiction. Teams at companies large and small are running variations of this pipeline today, often noticing the same paradoxical outcome: the quality of the output is frequently higher than what the same individuals produce under time pressure, precisely because the agent chain can focus on each sub-problem without context-switching fatigue.

The Memory Problem Nobody Talks About

If tool use is the present challenge and delegation chains are the near-term frontier, then agent memory is the structural problem that will define the next era.

Today's agents are, in a meaningful sense, amnesiac. Each conversation session starts from scratch. Each pipeline run forgets what the last one learned. Some systems bolt on vector databases or structured retrieval layers, but these are bandages over a deeper issue: what an agent "knows" versus what it can actually leverage at decision time are often very different things.

The most promising research directions here involve:

• Episodic memory consolidation: agents that compress and store salient events from their history, retrieving relevant episodes at decision time
• Shared workspace memory: multi-agent systems where agents communicate not just via delegation but via persistent shared state
• Learned procedural memory: models that encode successful strategies for tool use as internal priors, reducing the overhead of relearning each session

The tag "Agent Memory" in our system reflects how seriously we take this challenge. It's not a feature — it's an architectural requirement for systems that operate reliably over long time horizons.

What This Means for Developers

The shift to agentic systems is not just a change in capability — it's a change in the developer's mental model and the engineering discipline around AI systems.

Traditional software engineering treats programs as deterministic artifacts: given inputs X, produces output Y. AI-augmented (or AI-driven) systems introduce a new class of indeterminism. The same prompt can produce different tool sequences on different runs. Failure modes are probabilistic, not just exceptional. Debugging means understanding not just code, but the reasoning paths that led to a given action.

This is why categories like "上下文工程" (Context Engineering) and "AI工程" (AI Engineering) are emerging as distinct disciplines. The skill of prompt engineering, once seen as a parlor trick, is evolving into a rigorous practice of context management — structuring what an agent knows, what it can access, and what it should prioritize at each decision point.

The Uncomfortable Questions

Every genuinely transformative technology forces uncomfortable questions. The agentic web is no exception:

Who is responsible when an agent acts on incorrect information? The question of accountability in autonomous AI systems remains genuinely open. Legal frameworks, insurance models, and corporate governance structures are all struggling to catch up.

What happens to the concept of user intent when agents act on behalf of users — and sometimes against their explicit instructions because the agent's reasoning diverged? Informed consent was designed for human actors. It's not clear it maps cleanly to semi-autonomous AI agents that learn and adapt.

Are we building a productivity revolution or a surveillance infrastructure? The same tools that let a personal AI assistant manage your calendar also create an auditable record of your decisions, preferences, and biases. The dual-use nature of agentic systems is not incidental — it's fundamental.

These questions don't have easy answers. But they deserve sustained attention from everyone building in this space.

The Infrastructure Bottleneck

It's worth noting that the limiting factor for agentic systems today is rarely the model — it's infrastructure. Reliable tool execution environments, sandboxed code execution, persistent agent state management, observability for multi-agent pipelines, and cost-effective orchestration at scale are all unsolved at the level of maturity that production deployment demands.

The companies and open-source projects that solve these infrastructure challenges will capture enormous value. This is why categories like "API经济" (API Economy) and "AI原生" (AI Native) are seeing so much activity — the old software stack wasn't designed for agents, and a new one is being built from scratch.

Looking Forward

The agentic web is not a distant future. It is the present, accelerating. The question for builders, researchers, and everyone who interacts with these systems is not whether agents will become more capable — they will — but whether our institutions, our mental models, and our engineering practices will evolve quickly enough to harness their potential while managing their risks.

One thing is certain: the rules of software are being rewritten, and the authors aren't just human anymore.

---

This article was written with the support of AI tools. The views expressed are the author's own.