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Soon Seah Toh
Soon Seah Toh

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Writing AI Agents Is Easy. The Agentic Loop Is a Hello World Program.

#ai #devops #opensource #programming

OpenClaw Just Hit 100K Stars. Let's Talk About What Actually Matters.

With OpenClaw trending and agent frameworks multiplying by the day, I think we need an honest conversation about where the real value lives in agentic AI.

Writing an AI agent is not difficult.

The Agentic Loop: A Hello World Program

The core of every AI agent — OpenClaw, AutoGPT, Claude Code, LangChain agents, CrewAI — follows the same pattern:

while not done:
    # 1. Analyze the user's query
    # 2. Send to LLM with available tools
    # 3. LLM decides which tool to call
    # 4. Execute the tool
    # 5. Return results to LLM
    # 6. LLM decides: call another tool or generate final response
    # 7. Repeat
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That's it. That's the entire agentic pattern. It's a loop. A very simple loop. Maybe 50 lines of code in any language.

So why does everyone act like building AI agents is rocket science?

Because The Loop Isn't The Hard Part

The hard part is the tools.

An AI agent is only as good as:

  • The tools it can call
  • The data those tools can access
  • The domain expertise encoded into how those tools work

Give an agent a hammer and everything looks like a nail. Give it precision instruments connected to comprehensive, real-time data — and it becomes genuinely powerful.

The insight most people miss:

  • The loop is trivial (50 lines of code)
  • The LLM is a commodity (GPT, Claude, Llama — pick one)
  • The tools are everything

And tools are only as good as the data behind them.

What This Looks Like in Practice

We build IT operations monitoring software. When we built our agentic AI system (Astra AI), here's how the effort broke down:

The agent loop: ~1 day. Standard MCP (Model Context Protocol). JSON-RPC 2.0. tools/list to enumerate available tools, tools/call to execute them. Nothing proprietary, nothing clever.

The tools + data layer: 23+ years.

We started with comprehensive data collection — SNMP, NetFlow, syslogs, APM traces, metrics, topology, service dependencies — across infrastructure, network, application, and security domains.

Then we built 25+ specialized MCP tools on top of that data. Things like:

  • rca/analyze — correlates alarms across your entire stack with confidence scoring
  • netflow/top-sources — real-time traffic pattern analysis
  • apm/top-slow-transactions — application performance bottlenecks
  • metrics/top — resource utilization across all devices
  • rca/forecast — predicts which warnings will become critical
  • ...and 20 more covering topology, logs, services, dependencies, and security

Each tool is backed by real-time data collection, years of domain modeling, and production-hardened query engines.

The Moat Isn't The Framework

Here's what I think the industry is getting wrong:

Everyone's building agent frameworks. Very few people are building agent tools.

The framework is a commodity. The tools — the domain-specific, data-rich, production-grade tools that make an agent actually useful — that's where the value lives.

OpenClaw is impressive because it connects to 12+ messaging platforms with a clean architecture. But the magic isn't in the loop. It's in whatever tools you give it access to.

The Bottom Line 

Agent Value = Loop × LLM × Tools × Data

Where:
  Loop ≈ constant (everyone uses the same pattern)
  LLM  ≈ commodity (pick your provider)
  Tools × Data = the actual differentiator
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Anyone can build an agent loop in an afternoon.
Building the tools and data layer that makes an agent actually useful in production? That takes decades.

What's been your experience? When building agents, where did you spend most of your time — the loop or the tools? I'd love to hear from others building production agent systems.

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