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WebMEM™

The Protocol for Structuring, Delivering, and Conditioning Trust-Scored AI Memory on the Open Web

  • Primer
  • Memory-First
  • Protocols
    • SDT Specification
    • WebMEM SemanticMap
    • WebMEM MapPointer
    • Digest Endpoint Specification
    • ProvenanceMeta Specification
    • AI Retrieval Feedback Loop Specification
    • Semantic Feedback Interface (SFI) Specification
    • Glossary Term Protocol (GTP) Specification
    • Examples
  • RFC
  • Glossary
  • About
    • WebMEM License
    • Mission
    • Charter

Chapter 26: ProcedureFragments and DirectoryFragments

Structured Tasks and Indexed Entities for Agentic Execution

 

 

 

Agents aren’t just explaining anymore.

They’re:

  • Guiding users through enrollment
  • Recommending providers
  • Completing forms
  • Processing benefits
  • Comparing options
  • Resolving workflows

To do that, they don’t just need facts or logic.

They need step sequences and entity indexes—structured fragments they can follow and trust.

That’s where ProcedureFragments and DirectoryFragments come in.

 

What Is a ProcedureFragment?

A ProcedureFragment is a multi-step, logic-aware instruction set designed to guide an agent or user through a process—structured in YAML and embedded as memory.

This isn’t a long blog post that says, “First, do this…”

It’s an embedded, retrievable sequence of steps:

  • Each with a purpose
  • Optional gating
  • Conditional branches
  • Output expectations
  • Surface-agnostic formatting

Agents use these to:

  • Provide guided flows
  • Prevent hallucinated instructions
  • Cite tasks step-by-step
  • Execute logic with transparency

 

Example: ProcedureFragment

<template data-visibility-fragment>

FragmentType: ProcedureFragment

ProcedureID: medicare-plan-enrollment

Title: How to Enroll in a Medicare Advantage Plan

Steps:

– Step: Confirm eligibility

Instruction: Use the EligibilityFragment for Medicare Advantage to validate.

Requires: user.age, user.zip

– Step: Compare plans in service area

Instruction: Pull plans from DirectoryFragment: MA-Plans-2025-FL-32608

– Step: Select a plan

Instruction: Ensure selected plan includes required drug coverage

– Step: Enroll

Instruction: Submit application via plan provider or call 1-800-MEDICARE

– Step: Confirm enrollment

Instruction: Expect plan materials to arrive within 14 business days

ExpectedOutcome: Enrollment submission recorded

Provenance: MedicareWire Enrollment Guide, 2025

trust_layer: procedure-definition

</template>

 

Key Fields:

Field Description
Steps Ordered instructions, optionally linked to other fragments
Requires Pre-conditions or input gates
ExpectedOutcome What success looks like
Provenance Source of trust for the process
trust_layer Declares confidence and retrieval use case

This isn’t instruction text.
It’s an actionable semantic procedure.

 

What Is a DirectoryFragment?

A DirectoryFragment is a structured index of entities—optimized for retrieval by agents, not users—so that listings can be selected, filtered, or reasoned with by machines.

Think of it like:

  • A plan directory
  • A provider index
  • A services list
  • A network of structured entities

But instead of flat pages or JavaScript tables, you publish structured index blocks that can be retrieved and reasoned over.

 

Example: DirectoryFragment

<template data-visibility-fragment>

FragmentType: DirectoryFragment

Title: 2025 Medicare Advantage Plans in Alachua County, FL

EntityType: Medicare Advantage Plan

Entities:

– ID: H1234-005-2025

Name: WellCare Giveback (HMO)

Premium: $0

DrugCoverage: Yes

Rating: 4.0

MaxOutOfPocket: 5400

Dental: Comprehensive

CoverageArea: FL-32608

– ID: H5678-003-2025

Name: Aetna Value Plan (PPO)

Premium: $42

DrugCoverage: Yes

Rating: 3.5

MaxOutOfPocket: 5900

Dental: Preventive only

CoverageArea: FL-32608

Provenance: MedicareWire.com Plans Index, 2025

trust_layer: factual-core

</template>

Why It Works

This isn’t a full-blown API.
It’s an index snapshot with structured fields agents can ingest.

You can:

  • Publish one per county
  • Link them to Eligibility + ProcedureFragments
  • Cite plan IDs in Enrollment fragments
  • Update annually with versioned Provenance

It’s the retrieval-first version of a directory page.

And it works at scale—with zero JavaScript.

 

How These Fragments Work Together

Let’s map a common agent workflow:

“I want to enroll in a Medicare plan in my area.”

Agent Action Fragment Used
Validate user is eligible EligibilityFragment
Explain enrollment process ExplainerFragment or ProcedureFragment
Pull relevant plans DirectoryFragment
Check for drug coverage Field in entity block
Recommend a plan Based on logic + rating
Guide user to enroll ProcedureFragment, Step 4

You didn’t publish a chatbot.
You published a trust-driven decision workflow—in fragments.

 

Best Practices

  • Structure each ProcedureFragment with 3–7 steps
  • Link to glossary and eligibility fragments where relevant
  • Keep DirectoryFragment fields predictable (avoid nested arrays unless necessary)
  • Always declare Provenance and trust_layer
  • Publish to crawlable, static HTML surfaces (yes, even GitHub works)

 

Why This Is the Future

Because AI agents:

  • Don’t browse
  • Don’t click
  • Don’t scroll
  • Don’t guess

They:

  • Retrieve
  • Reason
  • Compare
  • Guide

And now they can do it using your fragments.

You don’t need to build a no-code platform or AI SaaS.

You just need to publish memory that knows what to do.

 

Final Word

You’ve now given the machine:

  • Definitions
  • Explanations
  • Conditional logic
  • Access gates
  • Contextual roles
  • And now—tasks it can perform with confidence

You’re not publishing web content anymore.

You’re authoring semantic task flows and machine-usable indexes.

Let’s finish strong in Chapter 27—PythonFragments: Executable Logic Inside Trusted Memory.

Primary Sidebar

Table of Contents

  • Prologue: The Day the Interface Changed
  • Introduction: Reflection Is the New Retrieval

Part I: Foundations of Agentic Visibility

  1. The Rise of Agentic Systems
  2. What Is Agentic System Optimization?
  3. AI Doesn’t Rank—It Reflects
  4. Embedded Memory Fragments
  5. Glossary Terms as Memory Anchors
  6. Trust Layers and Provenance Blocks

Part II: The Structure of Machine Memory

  1. The Four Layers of Visibility
  2. Semantic Reinforcement and Co-Citation
  3. From Fragments to Memory
  4. Visibility Drift and Reflection Decay
  5. Reinforcing Reflection
  6. Monitoring Your Reflection

Part III: The Trust Publisher's Role

  1. The Trust Publisher’s Role
  2. Building a Public Memory Graph
  3. Reflection Sovereignty

Part IV: Systems and Ethics

  1. Agent Archetypes
  2. Semantic Conditioning Techniques
  3. Public Memory as Civic Infrastructure
  4. Adversarial Trust
  5. The Trust Publisher Taxonomy
  6. The Ethics of Memory Curation
  7. Listening to the Agents

Part V: Functional Memory Publishing

  1. From Memory to Reasoning
  2. ExplainerFragments
  3. PolicyFragments, PersonaFragments, and EligibilityFragments
  4. ProcedureFragments and DirectoryFragments
  5. PythonFragments
  6. Functional Memory Design

  • The Visibility Code Manifesto
  • Epilogue: A Trust Layer for the Machine Age

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