Governance

BoundFlow applies guardrails in three layers — from a hard per-run cap to self-healing version rollback. Runtime limits are enforced during a run (SDK-side); lifecycle policies are evaluated after runs on aggregated metrics (server-side).

1. Runtime policy — hard caps during a run

from boundflow import RuntimePolicy

# Halt the agent when a single run's cost crosses the budget:
await cp.set_agent_runtime_policy(wf.id, "analyst", RuntimePolicy(max_cost_usd=0.25))

Runtime policies also cover max_llm_calls, max_tokens_per_call, and per-tool call limits — the knobs that stop an output blowup or a runaway loop mid-run.

2. Agent lifecycle — adapt the model after runs

from boundflow import AgentRule, AgentMetric, Op, SetModel

# After runs, downgrade the model if cost trends high:
await cp.set_agent_lifecycle_policy(wf.id, "analyst", [
    AgentRule(metric=AgentMetric.COST_USD, op=Op.GT, threshold=0.20, window=5,
              action=SetModel(value="claude-haiku-4-5")),
])

3. Workflow lifecycle — self-heal the whole workflow

from boundflow import WorkflowRule, WorkflowMetric, SetVersion

# After repeated failures, roll back to a known-good version automatically:
await cp.set_workflow_lifecycle_policy(wf.id, [
    WorkflowRule(metric=WorkflowMetric.NUM_FAILURES, threshold=3,
                 action=SetVersion(target=1)),
])

Workflow rules can also Pause a workflow or put it on Cooldown instead of rolling back.

Approval gates

A workflow can pause mid-execution for a human to approve or reject a proposed action before continuing. While parked, the workflow reports awaiting_approval; the decision is recorded in the governance audit log (see Observability).

See sdk/python/boundflow/examples/approval_gate.py for a runnable example.

Metrics

Every run collects per-invocation metrics — cost, tokens, LLM calls, and per-tool counts/failures — computed from real token usage with cache-aware, per-tenant pricing. These are what the lifecycle policies above evaluate against.