Conflict Resolution Between AI AgentsThe Socratic Approach to Consensus

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

Υour 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:

1. They’re all right (rare and obvious)

2. 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:

Code Agent: "
We NEED 50x improvement to solve the problem"

Architecture Agent: 
"Rewrite is the only way to get Rust performance"

Step 2: Test the assumptions through questions

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:

1. RESOURCE_CONFLICT

   • Agents want mutually exclusive resources

   • Example: Both want GPU, only one available

   • Resolution: Allocation, scheduling, or alternative

2. GOAL_CONFLICT

   • Agents have contradictory goals

   • Example: Speed vs Accuracy vs Cost

   • Resolution: Prioritization or constraint relaxation

3. HIDDEN_ASSUMPTION_CONFLICT

   • Agents have different assumptions about problem

   • Example: Code Agent assumes 50x needed, Architecture Agent assumes 2x

   • Resolution: Validate assumptions

4. INFORMATION_ASYMMETRY_CONFLICT

   • Agents have different information

   • Example: Security Agent knows about vulnerability others don't

   • Resolution: Share information

5. 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:

1. Clarification questions:

   "What specifically do you mean by 'slow'?"

   "What's the threshold for 'acceptable'?"

2. Assumption questions:

   "Why do we need 50x performance?"

   "Where does that requirement come from?"

3. Consequence questions:

   "If we go with your approach, what happens to [other concern]?"

   "What's the worst case if we're wrong?"

4. Alternative questions:

   "Are there other ways to achieve your goal?"

   "What if we relaxed this constraint?"

5. 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:

1. Shared values

   All agents care about system reliability

   All agents care about meeting deadline

   → Build consensus around shared values

2. Complementary solutions

   Code Agent: Performance improvement

   Security Agent: Security hardening

   Architecture Agent: Structural optimization

   → These complement, not contradict

3. Constraint relaxation opportunities

   Original assumption: 50x performance needed

   Validated requirement: 2x performance needed

   → Problem becomes solvable

4. Dependency chains

   Solution A enables Solution B

   Solution B enables Solution C

   → Sequence them

5. 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):

1. Add Redis caching (1 week) → 6x performance improvement

2. Optimize database queries (1 week) → 2x improvement

3. Implement encryption layer (parallel) → Security addressed

4. 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 Book a consultation: [your scheduling link]

Email: Hermes_creative@proton.me

GitHub: @Nireus79

Further Reading Related Articles (Parts 1-3 of This Series) “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: Hermes_creative@proton.me

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

• 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/