Chapter 23: Distributed Memory Networks

“The whole is greater than the sum of its parts—especially when those parts can share what they've learned.”

The Problem of Isolated Intelligence

Imagine a development team of five engineers, each with their own AI coding assistant. Developer A's assistant learns their coding preferences, discovers useful patterns, and builds up contextual knowledge. Developer B's assistant does the same—independently. So does C's, D's, and E's.

The problem? When Developer A's assistant discovers that the team prefers a particular error handling pattern, that insight doesn't transfer to Developer B's assistant. When C's assistant learns the team's naming conventions, D's and E's assistants remain ignorant.

Each AI operates as an island, creating knowledge silos instead of shared understanding.

The Power of Shared Memory

Distributed Memory Networks solve this by creating a shared pattern library that all agents can access. When one agent learns something valuable, every agent benefits.

Think of it like a team wiki that updates itself automatically—except instead of documentation, it stores learned patterns, preferences, and institutional knowledge.

Multi-Agent Architecture

Attune AI supports specialized agents that each focus on specific domains while accessing a common knowledge base:

• Code Review Agent (Level 4 empathy) - Anticipates code quality issues
• Test Generation Agent (Level 3) - Creates comprehensive test suites
• Documentation Agent (Level 3) - Maintains living documentation
• Security Agent (Level 4) - Proactively identifies vulnerabilities
• Performance Agent (Level 3) - Optimizes for speed and efficiency

How Pattern Sharing Works

Patterns flow through a continuous learning cycle:

1. Discovery - One agent identifies a useful pattern
2. Storage - Pattern is saved with metadata (confidence, context, success rate)
3. Suggestion - Other agents automatically receive relevant patterns
4. Validation - Global confidence updates based on adoption and feedback

Implementation Example

from attune import PatternLibrary, AgentTeam

# Initialize shared pattern library
library = PatternLibrary(storage="redis://localhost")

# Create specialized agents
team = AgentTeam(
    agents=[
        CodeReviewAgent(empathy_level=4),
        SecurityAgent(empathy_level=4),
        TestAgent(empathy_level=3),
    ],
    shared_library=library
)

# When code_review discovers a pattern...
pattern = Pattern(
    name="null_check_before_access",
    type="security",
    confidence=0.85,
    context="typescript"
)
library.contribute(pattern, agent="code_review")

# Security agent automatically receives it
relevant = library.query(
    agent="security",
    context="typescript"
)
# Returns: [null_check_before_access, ...]

Coordination Workflows

Different tasks require different coordination patterns:

1. Parallel Execution

Agents work simultaneously on the same task. The Code Review Agent analyzes structure while the Security Agent checks for vulnerabilities—at the same time.

2. Sequential Workflow

When dependencies exist, agents work in order. Documentation Agent runs after Code Review completes, incorporating the review findings.

3. Hierarchical Coordination

A coordinator agent manages sub-agents, distributing work and aggregating results. Best for complex, multi-phase tasks.

Performance Impact

Multi-agent coordination delivers measurable productivity gains when agents work in parallel:

Tasks with independent components (code review, security scanning, test generation running simultaneously) consistently achieve 40-50% faster completion than sequential execution. Tasks with sequential dependencies see smaller but still meaningful gains from pattern sharing and reduced context switching.

Conflict Resolution

What happens when agents disagree? The Security Agent recommends one approach while the Performance Agent suggests another. The framework resolves conflicts by considering:

• Team priorities - Security-first teams weight security patterns higher
• Context fit - How well does each pattern match the current situation?
• Confidence scores - Patterns with higher confidence win ties
• Recency - Recent patterns may better reflect current practices

Best Practices

• Specialize agents for specific domains—generalists dilute expertise
• Share patterns broadly through unified libraries
• Execute in parallel when tasks allow
• Monitor continuously to catch issues early
• Define clear conflict resolution rules for your team's priorities

Key Takeaways