Chapter 23 Interactive Demo
Explore how multiple AI agents coordinate through shared pattern libraries
12 patterns discovered
8 patterns discovered
15 patterns discovered
| Pattern | Type | Confidence | Contributor | Select for Conflict |
|---|---|---|---|---|
| Use list comprehension | performance | 85% | Code Reviewer | |
| Add null checks | best_practice | 92% | Code Reviewer | |
| Mock external services | testing | 78% | Test Generator | |
| SQL injection prevention | security | 95% | Security Analyzer | |
| Use explicit loop | style | 80% | Code Reviewer |
Combine all factors (default)
Selected 2 patterns for conflict resolution
Select patterns and click "Resolve Conflict" to see the resolution result
from attune import (
AttuneOS,
PatternLibrary,
ConflictResolver,
AgentMonitor,
)
# 1. Create shared infrastructure
library = PatternLibrary()
resolver = ConflictResolver()
monitor = AgentMonitor(pattern_library=library)
# 2. Create agent team with shared library
code_reviewer = AttuneOS(
user_id="code_reviewer",
target_level=4,
shared_library=library
)
test_generator = AttuneOS(
user_id="test_generator",
target_level=3,
shared_library=library
)
# 3. Resolve conflicts between patterns
resolution = resolver.resolve_patterns(
patterns=[pattern1, pattern2],
context={"team_priority": "readability"}
)
print(f"Winner: {resolution.winning_pattern.name}")
print(f"Reasoning: {resolution.reasoning}")
# 4. Monitor team collaboration
stats = monitor.get_team_stats()
print(f"Collaboration efficiency: {stats['collaboration_efficiency']:.0%}")Dive deeper into Distributed Memory Networks in Chapter 23 of the book