Conflict & Mediation Support

Scenario: A manager or HR partner is mediating a workplace conflict. They need to understand each party's perspective without bias, identify underlying interests rather than stated positions, and guide the conversation toward resolution. Unstructured conversations often make conflicts worse.

Patterns used:

• ConflictResolver — applies principled negotiation (interests, not positions)
• MoralDilemmaResolver (enterprise) — navigates situations where values genuinely conflict
• StakeholderMapper — maps each party's interests and what resolution looks like for them

Integration: Raw Context builds for separate party preparation + quality-gated synthesis

---

import mycontext
mycontext.activate_license("MC-ENT-YOUR-KEY")

from mycontext import Context
from mycontext.foundation import Directive, Guidance, Constraints
from mycontext.templates.free.specialized import ConflictResolver
from mycontext.templates.enterprise.ethical_reasoning import MoralDilemmaResolver
from mycontext.templates.free.planning import StakeholderMapper
from mycontext.intelligence import QualityMetrics

metrics = QualityMetrics(mode="heuristic")
QUALITY_GATE = 0.68


def prepare_mediation(conflict: dict) -> dict:
    conflict_brief = "\n".join(f"{k}: {v}" for k, v in conflict.items())

    conflict_ctx = ConflictResolver().build_context(
        conflict=conflict_brief,
        context_section="Apply principled negotiation: separate people from problem, focus on interests not positions",
    )
    dilemma_ctx = MoralDilemmaResolver().build_context(
        conflict=conflict.get("core_tension", conflict_brief),
        context_section="Is there a genuine values conflict? If so, what are the competing values?",
    )
    stakeholder_ctx = StakeholderMapper().build_context(
        project="Workplace conflict resolution",
        objective=f"Resolve: {conflict.get('summary', '')}",
    )
    stakeholder_ctx.knowledge = conflict_brief

    results = {}
    for name, ctx, question in [
        ("analysis", conflict_ctx, "What are the underlying interests of each party? What would genuine resolution look like?"),
        ("values", dilemma_ctx, "Is there a genuine values conflict? How might competing values be reconciled?"),
        ("stakeholders", stakeholder_ctx, "Map each party's position, interests, and what resolution means for them."),
    ]:
        score = metrics.evaluate(ctx)
        print(f"  {name}: {score.overall:.0%}")
        if score.overall < QUALITY_GATE:
            results[name] = f"Quality too low ({score.overall:.0%})"
            continue
        results[name] = ctx.execute(provider="openai").response

    # Build mediator guide
    mediator_ctx = Context(
        guidance=Guidance(
            role="Expert workplace mediator",
            rules=[
                "Do not take sides",
                "Reflect feelings before addressing facts",
                "Ask open questions to surface interests",
                "Look for superordinate goals both parties share",
            ],
        ),
        directive=Directive(content=(
            "Prepare a mediation session guide based on:\n\n"
            + "\n\n".join(
                f"=== {k.upper()} ===\n{v}"
                for k, v in results.items()
                if isinstance(v, str) and len(v) > 50
            )
        )),
        constraints=Constraints(
            must_include=["opening statement", "key questions for each party", "common ground", "resolution options"],
        ),
    )
    guide_score = metrics.evaluate(mediator_ctx)
    if guide_score.overall >= QUALITY_GATE:
        results["mediator_guide"] = mediator_ctx.execute(provider="openai").response

    return results


conflict = {
    "summary": "Two senior engineers in conflict over technical direction",
    "party_a": "Alice (backend lead): wants microservices migration for scalability. Feels expertise is being ignored.",
    "party_b": "Bob (platform engineer): believes monolith is adequate, migration is premature risk. Feels Alice is CV-building.",
    "core_tension": "Architectural philosophy vs. pragmatism, and mutual professional respect",
    "context": "8-person startup, 18 months from Series A. Two public disagreements in planning meetings.",
}

mediation = prepare_mediation(conflict)
print("=== ANALYSIS ===")
print(mediation["analysis"][:500])
print("\n=== MEDIATOR GUIDE ===")
print(mediation.get("mediator_guide", "")[:600])

What You Get

A mediation preparation pack:

• Conflict analysis: underlying interests of each party (Alice wants technical credibility and scalable infrastructure; Bob wants stability and no unnecessary risk — these are not incompatible)
• Values analysis: whether there is a genuine values conflict and how it might be reconciled
• Stakeholder map: what resolution looks like for each party, what they would never accept
• Mediator guide: opening statement, diagnostic questions for each party, identified common ground, resolution options

Mediators who prepare this way consistently reach resolution in fewer sessions with better compliance.