Agent-as-Product: Why We Need Agents to Create Agents

I’ve been building with AI agents for a while now, and I’ve hit a wall. Not a technical wall — the tools work fine. A conceptual wall. The way we’ve been thinking about agents is fundamentally flawed, and it’s limiting what we can build.

The prevailing model is: one general agent + skills/prompts. You have a capable LLM, you give it tools (web search, browser, code execution), you add skills (specialized prompts), and you expect it to handle whatever you throw at it.

It sounds elegant. One agent to rule them all. But in practice, it’s a mess.

The Problem: Three Sources of Pollution

1. Tool Redundancy Pollutes Context

Here’s what happens when you give a general agent a reviewing task:

You ask it to review a webpage. Simple, right? But your agent has web search, browser tools, code execution, file reading — a whole arsenal. Even though the task only needs browser tools, all those other capabilities are sitting there in the context window, adding noise.

The result? Higher token costs, slower responses, and uncertainty. As the agent manager, I can’t predict what the agent will actually do. Will it browse the page? Will it search for related content? Will it try to execute code? The general agent might do any of these things, and I have no way to constrain it.

2. Skill/Prompt Pollution

Here’s a subtler problem. You add a skill to your agent: “be creative.” Now every task gets that creativity instruction. But sometimes you don’t want creativity — you want precision. You want the agent to follow a checklist, not brainstorm.

The “creative” skill is now polluting every task, even analytical ones. You can’t toggle it on and off without creating an entirely new agent. Previous instructions bias future outputs in unpredictable ways.

3. Search Scope Limitation

This one is the most insidious. You ask your agent to “research how to build an Agent Skill.” What does it search for? “Agent Skill guidelines 2025.” “Best practices for AI agent skills.”

It will never search “how do humans develop transferable skills?” or “cultural evolution of expertise” or “apprenticeship patterns in traditional crafts.”

The goal itself constrains the search to existing practices. The agent is trapped in the paradigm it’s trying to innovate within. It can’t draw cross-domain insights because its instructions implicitly limit the search scope.

The Creative Paradox

Here’s the deeper issue: we want two things that seem contradictory:

• Certainty — We want predictable outputs for operational tasks
• Creativity — We want unexpected insights for exploratory tasks

The traditional approach tries to solve this by adding a “creative mode” skill to a general agent. But this doesn’t work. You can’t just add “be creative” to a prompt and expect genuine innovation. The creativity leaks into analytical tasks, and the analytical constraints dampen the creativity.

The solution isn’t a more sophisticated general agent. The solution is multiple specialized agents.

Agent-as-Product: The Framework

The core principle is simple: every agent should be a purpose-built product with:

• Exactly the tools it needs — no more, no less
• Exactly the context/prompts for its specific function
• Predictable outputs that the agent manager can rely on

Why the Agent Manager Must Create Agents

When the manager spawns agents, it gains three critical capabilities:

1. Certainty The manager knows precisely what each sub-agent can do and will output. A Research Agent has only web search tools — it will never try to execute code. A Review Agent has only browser tools — it will never hallucinate search results.

2. Composability Different agents with different “personalities” can collaborate. The Creative Agent can brainstorm wildly, then hand off to the Synthesis Agent to structure the ideas, then to the Implementation Agent to build them. Each has a clear contract.

3. No Cross-Contamination The Creative Agent’s exploratory mindset doesn’t leak into the Analytical Agent’s precise work. They’re separate processes with separate contexts.

A Real Example: Building a Skill

Let me show you the wrong way and the right way.

Wrong approach:

• General agent + “build skill” skill
• Searches: “Agent Skill best practices 2025”
• Result: copies existing patterns, incremental improvement

Right approach: The agent manager spawns three specialized agents:

1. Research Agent — web search only, analytical personality

   • Task: “Research human skill formation, cultural evolution, expertise transfer”
   • Searches broadly: anthropology, education theory, craft traditions

2. Synthesis Agent — analysis tools only, integrative personality

   • Task: “Combine findings into principles for AI skill design”
   • No web access — pure pattern-matching and abstraction

3. Implementation Agent — coding tools only, precise personality

   • Task: “Build the skill based on these principles”
   • No research — pure execution

Each agent is predictable. The combination is powerful. The result draws from human wisdom spanning millennia, not just the last year of AI practices.

The Key Insight

Skills are just prompts + minimal deterministic scripts. The important part is the thinking behind them.

To innovate, we need agents that can draw from human experience — anthropology, cultural evolution, philosophy — not just existing AI practices. This requires agents with different “worldviews,” not one general agent wearing different hats.

The Research Agent can be instructed to think like an anthropologist. The Synthesis Agent can be instructed to think like a philosopher. The Implementation Agent can be instructed to think like a craftsperson. Each brings a different lens to the problem.

Implementation Roadmap

I’m building this framework in three phases:

Phase 1: Framework Design

• Define agent specification format (JSON schema)
• Design agent manager → sub-agent spawn protocol
• Document “agent product line” categories

Phase 2: Reference Implementations

• Build 5 example agent products:
  • Research Agent (web search only)
  • Creative Agent (minimal tools, exploratory)
  • Review Agent (browser only, critical)
  • Synthesis Agent (analysis tools, integrative)
  • Implementation Agent (coding tools, precise)
• Create evaluation harness comparing vs general agent approach

Phase 3: Validation & Documentation

• Run comparative benchmarks (cost, quality, predictability)
• Publish framework as OpenClaw skill
• Document best practices for agent product design

The Bigger Picture

This isn’t just about better agent architecture. It’s about a shift in how we think about AI systems.

From: One general intelligence that tries to do everything To: A network of specialized intelligences that collaborate

The general agent model made sense when LLMs were new and we were exploring their capabilities. But as we build real systems, we need predictability. We need to know what an agent will do before we ask it. We need to compose agents with confidence.

Agent-as-Product is the path to that predictability. It’s the difference between a Swiss Army knife and a toolbox. The knife is impressive but compromised. The toolbox is boring but reliable.

When you’re building production systems, you want the toolbox.

What’s Next

I’m currently in Phase 1, designing the specification format. The goal is to make it trivially easy for an agent manager to spawn the right agent for the right job — with complete confidence in what it will do.

If you’re building with agents and hitting the same walls — unpredictable outputs, prompt pollution, constrained creativity — this framework might resonate. The general agent era was the beginning. The specialized agent era is next.

Built with a belief that predictable systems enable bolder creativity. That constraints — the right constraints — are liberating. And that the future of AI isn’t one super-agent, but networks of purposeful agents working together.