Data Quality Expectations for production agents

By Michael Samuel Naeem · March 2, 2025 · 6 min read

Data Quality Expectations for production agents means you make agent data quality expectations observable and interruptible — with a named owner, a measurable signal, and a rollback a tired on-call can run. I reach for this when the path is on a critical user journey; that is also when shortcuts like skipping metrics until the first incident start paging people.

This write-up is specific to agent-data-quality-expectations in a agent context, using Postgres, Redis, Temporal for the mechanics while keeping ownership human.

Incident pattern involving agent data quality expectations

Teams usually discover Data Quality Expectations for production agents after a quiet failure — wrong data, slow pages, or a bill spike. Design for the path is on a critical user journey.

Put a metric on the user-visible effect of agent data quality expectations before you optimize internals. If the path is on a critical user journey, you need that graph on day one.

Document what 'success' and 'undo' mean in product language. Future reviewers will not share your context on agent data quality expectations.

Slug-specific note (agent-data-quality-expectations): prioritize expectations behavior under load and verify with a fixture named agent-data-quality-expectations-smoke.

Root cause in plain language

I treat Data Quality Expectations for production agents as an operations problem first. The goal is to make agent data quality expectations observable and interruptible, not to collect frameworks.

With Postgres, Redis, Temporal, the mechanics are straightforward; the hard part is invariants. The anti-pattern I still see is skipping metrics until the first incident.

Ship behind a flag, canary by cohort, and write the rollback in the PR description. Data Quality Expectations for production agents that needs a hero is not done.

Concretely, being able to make agent data quality expectations observable and interruptible forces explicit choices: source of truth, timeout budgets, and which errors users see versus operators.

Slug-specific note (agent-data-quality-expectations): prioritize expectations behavior under load and verify with a fixture named agent-data-quality-expectations-smoke.

# Data Quality Expectations for production agents
from dataclasses import dataclass

@dataclass(frozen=True)
class AgentDataQualityERequest:
    tenant_id: str
    idempotency_key: str

async def run_agent_data_quality_expec(req, deps) -> None:
    if await deps.store.seen(req.idempotency_key):
        return
    with deps.tracer.start_as_current_span("agent-data-quality-expectations"):
        await deps.client.execute(req, timeout=2.0)
    await deps.store.mark(req.idempotency_key)

The fix that held under load

Agent loops amplify mistakes: one bad tool call can fan out across systems. For agent data quality expectations, that means making failure visible early.

Put a metric on the user-visible effect of agent data quality expectations before you optimize internals. If the path is on a critical user journey, you need that graph on day one.

Ship behind a flag, canary by cohort, and write the rollback in the PR description. Data Quality Expectations for production agents that needs a hero is not done.

My never-again list for agent data quality expectations: skipping metrics until the first incident; shipping without a kill switch; and alerting only on infrastructure CPU.

Slug-specific note (agent-data-quality-expectations): prioritize expectations behavior under load and verify with a fixture named agent-data-quality-expectations-smoke.

Tests and probes that catch regressions

Agent loops amplify mistakes: one bad tool call can fan out across systems. For agent data quality expectations, that means making failure visible early.

With Postgres, Redis, Temporal, the mechanics are straightforward; the hard part is invariants. The anti-pattern I still see is skipping metrics until the first incident.

Document what 'success' and 'undo' mean in product language. Future reviewers will not share your context on agent data quality expectations.

Review prompts I use: what happens twice, what happens never, what happens partially? If Data Quality Expectations for production agents cannot answer, it is not production-ready.

Slug-specific note (agent-data-quality-expectations): prioritize expectations behavior under load and verify with a fixture named agent-data-quality-expectations-smoke.

Runbook lines that save minutes

Teams usually discover Data Quality Expectations for production agents after a quiet failure — wrong data, slow pages, or a bill spike. Design for the path is on a critical user journey.

With Postgres, Redis, Temporal, the mechanics are straightforward; the hard part is invariants. The anti-pattern I still see is skipping metrics until the first incident.

Ship behind a flag, canary by cohort, and write the rollback in the PR description. Data Quality Expectations for production agents that needs a hero is not done.

Slug-specific note (agent-data-quality-expectations): prioritize expectations behavior under load and verify with a fixture named agent-data-quality-expectations-smoke.

Related reading:

• saga pattern distributed transactions
• webhooks reliable delivery
• designing for observability slos

Platform guardrails afterward

Agent loops amplify mistakes: one bad tool call can fan out across systems. For agent data quality expectations, that means making failure visible early.

With Postgres, Redis, Temporal, the mechanics are straightforward; the hard part is invariants. The anti-pattern I still see is skipping metrics until the first incident.

Acceptance check: an on-call engineer can explain system state for agent data quality expectations from one dashboard and one runbook page.

Slug-specific note (agent-data-quality-expectations): prioritize expectations behavior under load and verify with a fixture named agent-data-quality-expectations-smoke.

Practical defaults for Data Quality Expectations for production agents

I treat Data Quality Expectations for production agents as an operations problem first. The goal is to make agent data quality expectations observable and interruptible, not to collect frameworks.

Put a metric on the user-visible effect of agent data quality expectations before you optimize internals. If the path is on a critical user journey, you need that graph on day one.

Acceptance check: an on-call engineer can explain system state for agent data quality expectations from one dashboard and one runbook page.

Slug-specific note (agent-data-quality-expectations): prioritize expectations behavior under load and verify with a fixture named agent-data-quality-expectations-smoke.

In review, require a short failure note covering retry, partial deploy, and skipping metrics until the first incident. Missing that note blocks merge.

Review questions before merging agent data quality expectations work

I treat Data Quality Expectations for production agents as an operations problem first. The goal is to make agent data quality expectations observable and interruptible, not to collect frameworks.

Put a metric on the user-visible effect of agent data quality expectations before you optimize internals. If the path is on a critical user journey, you need that graph on day one.

Ship behind a flag, canary by cohort, and write the rollback in the PR description. Data Quality Expectations for production agents that needs a hero is not done.

Slug-specific note (agent-data-quality-expectations): prioritize expectations behavior under load and verify with a fixture named agent-data-quality-expectations-smoke.

After a month, delete unused flags and dual paths. agent-data-quality-expectations accumulates temporary bridges faster than teams expect.

Field notes after thirty days of agent data quality expectations

Teams usually discover Data Quality Expectations for production agents after a quiet failure — wrong data, slow pages, or a bill spike. Design for the path is on a critical user journey.

Keep side effects at the edges and make every write idempotent. Data Quality Expectations for production agents without retry semantics is a future incident write-up.

Document what 'success' and 'undo' mean in product language. Future reviewers will not share your context on agent data quality expectations.

Slug-specific note (agent-data-quality-expectations): prioritize expectations behavior under load and verify with a fixture named agent-data-quality-expectations-smoke.

In review, require a short failure note covering retry, partial deploy, and skipping metrics until the first incident. Missing that note blocks merge.

Resources

• Internal runbook seed: agent-data-quality-expectations
• https://12factor.net/
• https://martinfowler.com/