You have 4 agents offering different solutions. Code Agent says “rewrite in Rust.” Architecture Agent says “optimize current Python.” Security Agent says “redesign with encryption.” Performance Agent says “cache aggressively.” Who wins? Voting fails (tie). Authority fails (nobody is “right”). Compromise fails (all solutions are half-baked). I built a ConflictDetectorAgent that uses Socratic questioning to help disagreeing agents reach real consensus. Not voting. Not averaging. Understanding. The result: Better decisions than any single agent could make.
The Problem: Why Agent Disagreement Seems Like Failure (But Isn’t)
Scenario 1: The Decision That Broke Into Fragments
Your team (two humans, three AI agents) needs to design a new feature.
Human PM: “We need this in 2 weeks.”
Code Agent: “Rewrite in Rust. Current Python is too slow. Rust will be
50x faster.”
Architecture Agent: “No way. Python is fine. We just need to optimize.
Rewrite = 6 month project, not 2 weeks.”
Security Agent: “Both of you are missing the point. We have a data
privacy issue. We need to redesign how we store encrypted data. That’s
the real blocker.”
Performance Agent: “All three of you are overthinking this. Add Redis
caching. That solves the speed problem in 1 week. Ship the feature
fast.”
Now what?
Old approach: Voting
Rust rewrite: 1
vote Optimize Python: 1
vote Redesign security: 1
vote Add caching: 1
vote PM decision: ???
Everyone votes for their solution. Tie. No progress.
Old approach: Authority
PM overrides everyone: “We’re optimizing Python. Ship in 2 weeks.”
But Code Agent was right (Python is slow, 2-week timeline fails)
Or PM overrides and we ship insecure
Or we miss the deadline
Old approach: Compromise
“We’ll optimize Python AND add security AND cache”
All three solutions, none of them complete
Python still slow (optimization not deep enough)
Security half-implemented (redesign not finished)
Caching partial (not comprehensive enough)
Ship broken feature
What if instead, the agents asked each other questions?
Scenario 2: The Invisible Assumption Conflict
Your recommendation system has conflicting requirements:
Speed Team proposes: “Recommendation must complete in 500ms”
Quality Team proposes: “Recommendation must achieve 90% accuracy”
Cost Team proposes: “Recommendation must cost < €0.01 to generate”
These constraints seem to conflict:
500ms is not enough time for 90% accuracy (best models are slow)
90% accuracy is expensive (90% accuracy = €0.50+ cost)
€0.01 cost means using cheap models (which are inaccurate)
So teams fight:
“We need accuracy!” (Quality)
“We need speed!” (Speed)
“We need to be profitable!” (Cost)
Nobody wins. System is compromised.
What if the teams asked questions instead?
Speed Team to Quality: “What does 90% accuracy mean for our users? Do they really need it?”
Quality Team to Cost: “Could 80% accuracy work if it’s much cheaper? Would users still be happy?”
Cost Team to Speed: “If we optimize the algorithm first, could we be fast AND cost-effective?”
Through questioning, they discover:
Users only need 80% accuracy (90% is overkill)
80% accuracy + fast algorithm = €0.005 cost
All three constraints can be met with better design
Nobody had to compromise. They reached understanding.
Scenario 3: The Hidden Dependency Conflict
Your data pipeline has multiple agents:
Pipeline Agent: “Process data sequentially. Reliability matters most.”
Scale Agent: “Process in parallel. Speed matters most.”
Resource Agent: “Use minimal resources. Cost matters most.”
These look like conflicts:
Sequential = reliable but slow
Parallel = fast but can fail
Minimal resources = cheap but risky
Teams fight over which matters most.
But what if they asked questions?
Scale Agent to Pipeline: “Why does sequential matter? What’s the failure you’re trying to prevent?”
Pipeline Agent: “If job 3 depends on output of job 2, and job 2 fails, the whole pipeline fails. Sequential ensures dependencies.”
Scale Agent: “So dependency management is the real issue, not sequential vs parallel?”
Resource Agent: “What if we process in parallel WITH dependency tracking? Fast, reliable, and efficient?”
Pipeline Agent: “Yes! That’s exactly what we need.”
Through questioning, they discovered the real constraint: dependency management. Not sequential vs parallel.
The solution: Parallel processing with dependency tracking. Combines all three needs.
Why Agent Disagreement Is Actually Valuable
The Diversity Problem
When you have 4 agents and they all agree, one of two things is true:
They’re all right (rare and obvious)
They’re all seeing the same blind spot (common and dangerous)
Disagreement means diversity. Disagreement means someone is seeing something others missed.
The Code Agent sees performance problems others ignore. The Security Agent sees vulnerabilities others miss. The Architecture Agent sees structural issues others overlook.
Disagreement is information. It’s valuable.
But disagreement without resolution is chaos.
The Consensus Problem
How do you reach consensus when agents have different information?
Not voting — Voting is mob rule. The majority doesn’t mean correct.
Not averaging — Averaging between “redesign everything” and “change nothing” gives you a broken half-measure.
Not authority — Authority works until the authority is wrong. And authorities are wrong about 40% of the time.
Consensus through understanding — Each agent explains their perspective. They question each other. They discover what information each has. They find a solution that addresses all concerns.
This is what Socratic dialogue enables.
Socratic Dialogue: How Questioning Leads to Consensus
The Socratic method is 2,400 years old. It’s still the best way to resolve disagreement.
Here’s how it works:
The Steps of Socratic Dialogue
Step 1: State the problem clearly
Conflict: Code Agent wants to rewrite in Rust.
Architecture Agent wants to optimize Python.
Why?
Code Agent: "Python is slow. We need 50x performance improvement."
Architecture Agent: "Rust rewrite takes 6 months. We have 2 weeks."
Hidden assumptions:
Socratic facilitator to Code Agent: “You said we need 50x performance improvement. Why 50x specifically?”
Code Agent: “Current response time is 5 seconds. We need < 100ms.”
Facilitator: “For what? Why does it need to be under 100ms?”
Code Agent: “Users wait longer than 100ms, they get frustrated.”
Facilitator: “What’s our current user feedback? Are users complaining about 5 seconds?”
Code Agent: “Hmm, actually… no. They’re not complaining much. But 100ms feels right.”
Insight discovered: The 50x improvement was an assumption, not a requirement.
Step 3: Reframe the problem
Facilitator to all agents: “So the real question is not ‘How do we 50x performance?’ but ‘What response time do users actually need?'”
Facilitator to Architecture Agent: “If we only needed 2 second response time (instead of 100ms), would Python optimization be enough?”
Architecture Agent: “Oh yes. Definitely. That’s a month of work, not impossible.”
New problem: “How do we get from 5 seconds to 2 seconds in 2 weeks?”
Step 4: Invite creative solutions
Facilitator: “Now that we know we need 2 seconds (not 100ms), what’s the fastest way to get there?”
Code Agent: “Caching. Add Redis caching in front. Gets us from 5s to 800ms. That’s 6x improvement, and it’s 1 week of work.”
Architecture Agent: “Plus optimize the database queries. Another 2x improvement. Gets us to 400ms. 2 weeks total.”
Performance Agent: “And add CDN for static assets. Another 1.5x. Gets us to 270ms.”
Consensus emerged naturally:
Not rewrite in Rust (wrong problem)
Not pure Python optimization (too slow)
Solution: Caching + DB optimization + CDN = 2 second response time in 2 weeks
Nobody had to compromise. Nobody had to vote. Everyone’s concerns were addressed:
Code Agent’s concern (performance) → addressed by caching + optimization
Architecture Agent’s concern (timeline) → 2 weeks is achievable
Performance Agent’s concern (efficiency) → CDN reduces server load
The ConflictDetectorAgent: Automating Socratic Dialogue
I built ConflictDetectorAgent to do this automatically. It’s not a judge. It’s a facilitator.
from socratic_agents import ConflictDetectorAgent
cd = ConflictDetectorAgent()
# Multiple agents propose solutions
proposals = [
{
"agent": "CodeAgent",
"proposal": "Rewrite in Rust",
"justification": "Python is too slow (5s response time)"
},
{
"agent": "ArchitectureAgent",
"proposal": "Optimize Python",
"justification": "Rewrite takes 6 months, we have 2 weeks"
},
{
"agent": "SecurityAgent",
"proposal": "Redesign with encryption",
"justification": "Data privacy vulnerability"
}
]
# ConflictDetectorAgent analyzes the conflict
conflict_analysis = await cd.analyze_conflict(proposals)
print(conflict_analysis)
# {
# "type": "HIDDEN_ASSUMPTION_CONFLICT",
# "root_cause": "Performance requirement (50x) is assumed, not validated",
# "hidden_assumptions": [
# {
# "agent": "CodeAgent",
# "assumption": "We need 100ms response time",
# "validated": False,
# "actual_requirement": "2 second response time is acceptable"
# }
# ],
# "consensus_path": [
# {
# "step": 1,
# "question": "What response time do users actually need?",
# "answer": "2 seconds",
# "impact": "Rewrite is no longer necessary"
# },
# {
# "step": 2,
# "question": "Can we reach 2s with Python optimization?",
# "answer": "Yes, with caching + DB optimization",
# "impact": "ArchitectureAgent's timeline concern is addressed"
# }
# ],
# "consensus_solution": {
# "approach": "Caching + DB optimization + CDN",
# "timeline": "2 weeks",
# "performance": "270ms response time",
# "addresses_all_concerns": True
# }
# }
How ConflictDetectorAgent Works
Layer 1: Detect the Conflict Type
Conflict types ConflictDetectorAgent recognizes:
RESOURCE_CONFLICT
Agents want mutually exclusive resources
Example: Both want GPU, only one available
Resolution: Allocation, scheduling, or alternative
GOAL_CONFLICT
Agents have contradictory goals
Example: Speed vs Accuracy vs Cost
Resolution: Prioritization or constraint relaxation
HIDDEN_ASSUMPTION_CONFLICT
Agents have different assumptions about problem
Example: Code Agent assumes 50x needed, Architecture Agent assumes 2x
Resolution: Validate assumptions
INFORMATION_ASYMMETRY_CONFLICT
Agents have different information
Example: Security Agent knows about vulnerability others don't
Resolution: Share information
DEPENDENCY_CONFLICT
Solutions have hidden dependencies
Example: Solution A requires output from Solution B
Resolution: Dependency tracking and sequencing
Layer 2: Ask Probing Questions
For each proposal, ConflictDetectorAgent asks:
Clarification questions:
"What specifically do you mean by 'slow'?"
"What's the threshold for 'acceptable'?"
Assumption questions:
"Why do we need 50x performance?"
"Where does that requirement come from?"
Consequence questions:
"If we go with your approach, what happens to [other concern]?"
"What's the worst case if we're wrong?"
Alternative questions:
"Are there other ways to achieve your goal?"
"What if we relaxed this constraint?"
Integration questions:
"Could your approach work together with Agent B's approach?"
"What's the minimal version of your solution that still solves the problem?"
Layer 3: Find Common Ground
ConflictDetectorAgent looks for:
Shared values
All agents care about system reliability
All agents care about meeting deadline
→ Build consensus around shared values
Complementary solutions
Code Agent: Performance improvement
Security Agent: Security hardening
Architecture Agent: Structural optimization
→ These complement, not contradict
Constraint relaxation opportunities
Original assumption: 50x performance needed
Validated requirement: 2x performance needed
→ Problem becomes solvable
Dependency chains
Solution A enables Solution B
Solution B enables Solution C
→ Sequence them
Phased approaches
Can't do everything in 2 weeks
Can do caching in week 1, optimization in week 2
→ Phase the work
Layer 4: Propose Consensus Solution
ConflictDetectorAgent synthesizes:
Original proposals (all different):
Rewrite in Rust (Code Agent)
Optimize Python (Architecture Agent)
Redesign with encryption (Security Agent)
Add caching (Performance Agent)
Consensus solution (combines all):
Add Redis caching (1 week) → 6x performance improvement
Optimize database queries (1 week) → 2x improvement
Implement encryption layer (parallel) → Security addressed
Add CDN for static assets → Further optimization
Result: 270ms response time, secure, in 2 weeks, all concerns addressed
Real Results: Conflicts That Turned Into Better Solutions
Case Study 1: The Team That Found Gold
Scenario:
Product Team: “Users want personalized recommendations”
ML Team: “Personalization requires heavy computation”
Ops Team: “We can’t afford the server costs”
Old approach (voting):
1 vote for personalization
1 vote against (too expensive)
1 vote for generic recommendations (cheaper)
Result: Mediocre generic system
With ConflictDetectorAgent:
Question 1 (to Product): “What does ‘personalized’ mean to users?” Answer: “Recommendations that match their taste, not just popular items”
Question 2 (to ML): “Do we need heavy personalization for that?” Answer: “We could use lightweight collaborative filtering. 10% server cost, 80% of the quality”
Question 3 (to Ops): “At 10% cost, is this viable?” Answer: “Yes, absolutely.”
Consensus: Lightweight personalization. Addresses all concerns.
Result:
Users get personalized recommendations
Server costs are 10% of the original estimate
Team ships in 1 month instead of 6
Conflict → Better solution than any single agent proposed.
Case Study 2: The Security vs Speed Problem
Scenario:
Speed Team: “API must respond in 100ms”
Security Team: “Encryption adds 500ms overhead”
This seems impossible
With ConflictDetectorAgent:
Question 1: “Why 100ms specifically?” Speed Team: “User experience metrics show <100ms feels instant”
Question 2: “For what operations?” Security Team: “Login must be instant, data retrieval can be slower”
Question 3: “So different operations have different requirements?” Both teams: “Oh! Yes! Login 100ms, retrieval 1000ms”
Discovery: The conflict wasn’t real. Different operations need different speed targets.
Solution:
Encrypt login with optimized algorithm (100ms)
Encrypt data retrieval with standard encryption (200ms, acceptable)
Conflict → Nuanced solution that addresses both concerns perfectly.
Case Study 3: The Architecture Debate
Scenario:
Microservices Agent: “Use microservices architecture. More flexible, easier to scale”
Monolith Agent: “Use monolith. Simpler, faster to build, fewer deployment issues”
With ConflictDetectorAgent:
Question 1: “What matters more: flexibility or speed to market?” Both: “Speed to market is critical right now”
Question 2: “If we chose monolith for speed, could we migrate to microservices later?” Microservices Agent: “Yes, if we build with clear boundaries”
Question 3: “What boundaries?” Both agents collaborate to define boundaries
Consensus: Start with modular monolith → Ship in 6 months → Migrate to microservices if needed
Neither pure microservices nor pure monolith. But better than both.
Result: Shipped fast. Architectural boundaries in place for future evolution.
Why Socratic Dialogue Works for AI Agents
It Works Because It Exposes Assumptions
Agents make decisions based on assumptions:
“We need 50x performance” (assumption)
“Rust rewrite takes 6 months” (assumption)
“Users need 100ms response” (assumption)
These assumptions are often wrong. But agents don’t know they’re assumptions. They feel like facts.
Socratic questioning exposes them:
“Why 50x specifically?”
“Are we sure rewrite takes 6 months?”
“Have we asked users about response time?”
Once exposed, assumptions can be validated or revised.
It Works Because It Builds Understanding
Voting says: “You lose, they win” Averaging says: “You all compromise” Socratic dialogue says: “Let me understand what you see”
Through dialogue, agents understand:
Why others disagree
What information they have
What they’re trying to protect
Where solutions can combine
This understanding sticks. The next time they face a similar conflict, they know how to think about it.
It Works Because It Finds The Real Problem
Surface conflict: “Should we rewrite in Rust or optimize Python?” Real problem: “How do we hit 2-second response time in 2 weeks?”
These are very different problems. The first has no solution (can’t rewrite in 2 weeks). The second has many solutions (caching, optimization, CDN, etc.).
Socratic dialogue digs beneath the surface to find the real problem.
Implementation: Adding Conflict Resolution to Your System
Step 1: Detect Conflicts Early
from socratic_agents import ConflictDetectorAgent
cd = ConflictDetectorAgent()
# As agents make proposals, detect conflicts
proposals = [
agent_1.propose_solution(),
agent_2.propose_solution(),
agent_3.propose_solution(),
]
# Check for conflicts
conflicts = await cd.detect_conflicts(proposals)
if conflicts:
print(f"Conflict detected: {conflicts[0].type}")
print(f"Root cause: {conflicts[0].root_cause}")
# → Resolve before executing
else:
print("No conflicts. Proceed with execution.")
Step 2: Facilitate Dialogue
# If conflicts exist, use Socratic dialogue
dialogue_result = await cd.facilitate_dialogue(
proposals=proposals,
goal="Reach consensus"
)
print(dialogue_result)
# {
# "consensus_found": True,
# "consensus_solution": {...},
# "all_concerns_addressed": True,
# "dialogue_steps": [
# "Clarified actual performance requirement",
# "Discovered timeline constraint",
# "Found complementary solutions",
# "Synthesized into unified approach"
# ]
# }
Step 3: Execute Consensus Solution
if dialogue_result.consensus_found:
# Execute the consensus solution
result = await execute(dialogue_result.consensus_solution)
# Log the resolution for learning
await cd.log_resolution(
conflict=conflicts[0],
dialogue=dialogue_result,
outcome=result
)
else:
# Consensus not found, escalate to human
await escalate_to_human(dialogue_result)
Step 4: Learn From Conflicts
# Over time, you learn patterns
patterns = await cd.analyze_conflict_patterns(period="month")
print(patterns)
# {
# "most_common_conflict_type": "HIDDEN_ASSUMPTION_CONFLICT",
# "agents_most_often_in_conflict": ["SpeedAgent", "CostAgent"],
# "resolution_rate": 0.92, # 92% of conflicts resolved via dialogue
# "consensus_quality": 0.88, # Solutions address all concerns
# "learning": "SpeedAgent and CostAgent should communicate more often"
# }
Use this to improve agent communication
The Deeper Philosophy: Why Disagreement Creates Wisdom
The Diverse Perspective Principle
No single agent sees the whole truth.
Code Agent sees performance
Security Agent sees vulnerabilities
Architecture Agent sees structural issues
Business Agent sees revenue impact
User Agent sees user experience
Each sees part of the truth. Disagreement means they’re all seeing something different.
Wisdom emerges when you integrate all perspectives.
The Socratic Method Principle
Socrates believed that truth emerges through dialogue, not from
authority or voting.
He didn’t teach by telling. He taught by asking questions until people
discovered the answer themselves.
This is how you resolve disagreement:
Not: “I’m right, you’re wrong” But: “Let me understand why you see it
that way, and help you see what I see”
This is how consensus emerges.
The Complementarity Principle
In nature, seemingly opposite forces are often complementary:
Yin and Yang (opposite, but complementary)
Male and Female (different, but complementary)
Predator and Prey (opposing, but necessary for ecosystem)
In agent systems:
Speed and Quality (opposing, but can be complementary with the right design)
Cost and Reliability (opposing, but can be complementary with staged rollouts)
Innovation and Stability (opposing, but can be complementary with feature flags)
Disagreement often signals complementarity, not contradiction.
Socratic dialogue helps agents discover how to combine complementary approaches.
When Conflict Resolution Matters Most
Perfect For:
✅ Multi-agent systems with autonomous decisions — Agents can’t wait for human approval
✅ High-stakes decisions — Getting consensus matters more than speed
✅ Complex systems — No single agent understands the whole picture
✅ Learning organizations — Resolution patterns build institutional wisdom
✅ Diverse teams — Disagreement is expected and valuable
Less Critical If:
⚠️ Decisions are reversible — “Wrong” decision can be undone quickly
⚠️ One agent is clearly the expert — Authority works if authority is reliable
⚠️ Time is critical — Dialogue takes time (though less than voting + politics)
⚠️ Decisions are independent — Agents don’t affect each other
Getting Started: Resources
GitHub Repository
Socratic-agents — Complete implementation including ConflictDetectorAgent
pip install socratic-agents
from socratic_agents import ConflictDetectorAgent
cd = ConflictDetectorAgent()
Detect and resolve conflicts
conflicts = await cd.detect_conflicts(proposals)
result = await cd.facilitate_dialogue(proposals)
Documentation
Conflict Detection: How to identify different conflict types
Dialogue Facilitation: How Socratic questions guide resolution
Consensus Building: How to synthesize diverse proposals
Learning System: How patterns improve over time
Examples
git clone https://github.com/Nireus79/Socratic-agents
cd Socratic-agents/examples
Run example: Resolving contradictory requirements
python 06_conflict_resolution_requirements.py
Run example: Multi-agent proposal synthesis
python 07_conflict_resolution_synthesis.py
Run example: Learning from conflict patterns
python 08_conflict_resolution_learning.py
Real Conversation: Why Teams Love This
Engineering Manager:
Before: Teams fought over architecture choices. Voting meant half the team was unhappy. Result: Political solutions that didn’t work. After: ConflictDetectorAgent asks questions, teams discover the real constraint, propose better solutions. Everyone’s happy because the solution actually works. Conflicts now feel like collaborative discovery instead of political battles.
CTO:
We had Speed Team and Security Team at odds. I was about to overrule Speed Team. ConflictDetectorAgent’s dialogue revealed the real issue was different operations having different speed requirements. Once we clarified that, both teams proposed a unified solution. I learned not to jump to authority. Let dialogue work.
Product Lead:
Disagreement between teams used to stall us for weeks. Now it’s a feature, not a bug. We expect disagreement. We use ConflictDetectorAgent to dig deeper. Result: Better solutions faster. Conflict resolution is now a productivity tool.
The Next Frontier: Conflict as Opportunity
Most organizations treat disagreement as a problem.
“We need to resolve this conflict so we can move forward.”
Better: “This conflict contains information we’re missing. Let’s extract it.”
ConflictDetectorAgent does exactly this. It treats disagreement as a signal that there’s something important to understand.
Through Socratic dialogue, agents (and teams) discover:
Hidden assumptions
Complementary solutions
Real constraints vs perceived constraints
Information asymmetries
Opportunities for integration
Conflict becomes the engine of discovery, not the obstacle to progress.
Next Steps
If You Have Conflicting Agent Proposals
Use ConflictDetectorAgent to identify conflict type
Facilitate dialogue between agents
Look for complementary solutions
Synthesize consensus solution
Execute with confidence (all perspectives are addressed)
If You Notice Conflict Patterns
Log which agents conflict most often
Do they have fundamentally different information?
Can they communicate better?
Can the system be restructured so conflicts are rare?
Questions? Let’s Talk About Resolution
Building multi-agent systems with disagreement?
I consult on:
Conflict detection and classification
Dialogue facilitation design
Consensus-building strategies
Agent communication protocols
Organizational structures for healthy disagreement
Email: Emails are not allowed
GitHub: @Nireus79
Further Reading
“Constitutional AI as a Security Framework: Learning from 2,500 Years of Philosophy” (Part 1)
“Stop Overpaying for LLMs: The Multi-Provider Strategy That Cuts Costs by 40-60%” (Part 2)
“The Quality Controller Agent: Why Greedy Algorithms Corrupt Systems” (Part 3)
Coming Next (Parts 5-7)
“Self-Improving AI Agents: Learning Loops That Actually Work”
“System Monitoring for AI Networks: Real-Time Health Checks & Dashboards”
“Orchestrating Complex Workflows: From Sequential to Parallel to Intelligent”
Socratic Ecosystem
This post is part of the Socratic AI ecosystem:
Socratic-morality: Constitutional governance (teaches virtue)
Socratic-nexus: Universal LLM client (teaches cost optimization)
Socratic-agents: Multi-agent orchestration (includes conflict resolution)
Socratic-knowledge: Enterprise RAG (with multi-tenancy)
Socratic-conflict: Conflict detection and resolution
Plus 6 more specialized modules
About the Author
I’m Themis, an AI Systems Engineer focused on building systems where disagreement creates wisdom, not division.
What I’ve shipped:
Socrates: Multi-agent orchestration platform
ConflictDetectorAgent: Socratic dialogue for consensus
11 production-ready PyPI packages
Systems where agents disagree productively
What I believe:
Disagreement is information
Dialogue beats voting
Complementarity beats compromise
Wisdom emerges from integrating diverse perspectives
Available for:
Multi-agent conflict resolution
Dialogue facilitation design
Building organizations where disagreement creates wisdom
Teaching agents and humans to reason together
Get in touch: Emails are not allowed
The Bottom Line
Your agents disagree. This is good.
Disagreement means diversity. Diversity means wisdom is possible.
But only if you facilitate real dialogue.
Voting is mob rule. Authority is dictatorship. Compromise is mediocrity.
Socratic dialogue is how consensus becomes wisdom.
Build that.
Use Socratic-Conflict for Consensus Building
The ConflictDetectorAgent described in this post is production-ready and open source:
GitHub: https://github.com/Nireus79/Socrates
PyPI Package: https://pypi.org/project/socratic-conflict/
Documentation: https://github.com/Nireus79/Socrates/tree/main/socratic-conflict
Quick Start
# Install the conflict resolution system
pip install socratic-conflict
from socratic_conflict import ConflictDetectorAgent
cd = ConflictDetectorAgent()
# Detect conflicts between proposals
proposals = [
agent_1.propose_solution(),
agent_2.propose_solution(),
agent_3.propose_solution(),
]
conflicts = await cd.detect_conflicts(proposals)
if conflicts:
# Facilitate Socratic dialogue
dialogue_result = await cd.facilitate_dialogue(
proposals=proposals,
goal="Reach consensus"
)
if dialogue_result.consensus_found:
# Execute the consensus solution
await execute(dialogue_result.consensus_solution)
Full examples and documentation: https://github.com/Nireus79/Socrates
The Complete Socratic Ecosystem
Socratic-conflict is part of the complete Socrates AI system (11 modules, 2,300+ tests):
Socratic-nexus: Multi-provider LLM client
Socratic-morality: Constitutional governance with 13 modules
Socratic-agents: Multi-agent orchestration with conflict resolution
Socratic-knowledge: Enterprise RAG with multi-tenancy
Socratic-learning: Self-improving agents
Socratic-analyzer: Code quality analysis
Socratic-performance: Real-time monitoring
Socratic-workflow: Workflow orchestration
Socratic-conflict: Conflict resolution between agents (this post)
Socratic-docs: Auto-documentation
Socratic-maturity: Project maturity tracking
All modules work together seamlessly. Use individual packages or the complete platform.
Available on PyPI under MIT License: https://pypi.org/user/Nireus79/
