Harness orchestration

from pydantic import BaseModel
from agentfield import Agent, HarnessConfig

# No provider, no model -- this agent's harness calls run on CodeAF.
app = Agent(node_id="migrator", harness_config=HarnessConfig())

class MigrationPlan(BaseModel):
    sql_statements: list[str]
    rollback_steps: list[str]
    risk_assessment: str

@app.reasoner()
async def plan_migration(description: str) -> dict:
    # Harness reads the schema, writes SQL, validates against the Pydantic model
    result = await app.harness(
        f"Analyze the database schema and produce a migration plan for: {description}",
        schema=MigrationPlan,
        max_turns=20,   # turn cap -- CodeAF exec receives --max-turns
    )

    if result.is_error:
        return {"error": result.failure_type, "message": result.error_message}

    plan: MigrationPlan = result.parsed
    return {
        "sql": plan.sql_statements,
        "rollback": plan.rollback_steps,
        "cost_usd": result.cost_usd,
        "turns": result.num_turns,
    }

# Swap workers per-call -- Codex for test generation, Gemini for big refactors
@app.reasoner()
async def write_tests(module: str) -> dict:
    result = await app.harness(
        f"Write a comprehensive test suite for {module}.",
        provider="codex",
        model="o4-mini",
        max_turns=40,
    )
    return {"output": result.text, "cost_usd": result.cost_usd}

app.run()

# Same reasoner, different worker -- and claude-code is the provider that enforces max_budget_usd.
plan = await app.harness(
    prompt, schema=MigrationPlan, provider="claude-code", max_budget_usd=1.00
)

# Or set it once for the whole agent.
app = Agent(node_id="migrator", harness_config=HarnessConfig(provider="claude-code"))