Why AI Agents Need Composable Pipelines: Lessons from Building a Real System ​

The promise of AI agents is autonomy—systems that can break down complex tasks, execute them, and deliver results without constant human intervention. But as anyone who's built production AI systems knows, the gap between "agent that can do anything" and "agent that reliably does something useful" is vast.

After building PAI (Personal AI Infrastructure), I've learned that the secret isn't smarter models or better prompts. It's composable pipelines—small, focused actions that chain together predictably.

The Monolithic Agent Problem ​

Most AI agent frameworks start with a seductive premise: give the LLM tools and let it figure out the rest. Need to write a blog post? The agent can search the web, draft content, check grammar, optimize for SEO, and publish—all in one magical invocation.

This works great in demos. In production, it fails in fascinating ways:

• Unpredictable execution paths - The agent might check grammar before writing, or skip SEO entirely
• No intermediate checkpoints - When something fails at step 7, you lose steps 1-6
• Impossible to debug - Which part of the agent's reasoning caused the bad output?
• Can't improve incrementally - You can't upgrade just the grammar-checking without touching everything

The monolithic agent is a black box with too much responsibility.

The Unix Philosophy for AI ​

Unix solved this problem decades ago: do one thing well, use text streams to compose.

PAI applies the same principle to AI workflows:

Actions     = Small, focused units (parse, validate, transform)
Pipelines   = YAML definitions chaining actions sequentially
JSON I/O    = Universal data contract between steps

A blog publishing pipeline becomes:

steps:
  - id: draft
    action: blog/write-draft
    input:
      topic: "{{input.topic}}"

  - id: proofread
    action: blog/proofread
    input:
      content: "{{steps.draft.output.draft}}"

  - id: validate
    action: blog/validate
    input:
      content: "{{steps.proofread.output.corrected}}"

Each action has a defined input schema, output schema, and single responsibility. The pipeline runner handles data flow via template interpolation.

Why This Works Better ​

Predictable execution: Steps run in order. Always. You know exactly what will happen.

Checkpoint by default: Each step's output is captured. If step 3 fails, you have steps 1-2's results to inspect.

Debuggable: Which action produced bad output? Check its input/output. Fix that action. Done.

Incrementally upgradeable: Want better proofreading? Swap the blog/proofread action. Nothing else changes.

Testable in isolation: Each action can be unit tested with mock inputs. No need to run the whole pipeline.

Real-Time Visibility with Kanban ​

Composable pipelines also enable something monolithic agents can't: real-time progress visualization.

Because each step is discrete, we can build a Kanban board where:

• Columns represent actions (format, validate, enhance)
• Cards represent pipeline executions
• Cards move through columns as steps complete

Multiple agents can run different pipelines in parallel, and you watch them progress across the board. No more "agent is thinking..." for 30 seconds with no feedback.

The Trade-off ​

Composable pipelines require more upfront design. You must:

• Define clear action boundaries
• Design JSON schemas for inputs/outputs
• Write the pipeline YAML

But this investment pays dividends in reliability, debuggability, and maintainability. You're trading "maybe magic" for "definitely works."

Conclusion ​

The most capable AI agents won't be the ones with the most tools or the biggest context windows. They'll be the ones with the best composition primitives.

Small actions. Clear contracts. Predictable pipelines. Real-time visibility.

That's how you build AI systems that actually work in production.

Built with PAI - Personal AI Infrastructure