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Build a 24/7 Workflow: Async Automation Playbook for the Always-On Economy

Build 24/7, async workflows with AI agents. Learn queues, idempotency, SLAs, handoffs, and five playbooks to run safely while you sleep.
HJ
TaskFoundry0 mins read
Realistic night office with unattended dashboards monitoring 24/7 AI automation and task queues

The “always-on economy” doesn’t sleep—and neither should your workflows. This article is a field guide to building asynchronous, 24/7 automation with AI agents that run continuously, hand off work to humans when needed, and keep costs and risks under control. Instead of another agents 101, we focus on operational design: queues, retries, idempotency, SLO/SLA, escalation rules, and failure recovery you can actually deploy.

Table of Contents

Table of Contents

Why the Always-On Economy Needs Async Automation

Customers submit forms at 2 a.m., invoices arrive on Sunday, and web data changes while your team sleeps. The gap is not a staffing problem but a synchronization problem across time zones. Asynchronous systems buffer, queue, and process independently, then notify the right person only when action is truly required.

Aspect Synchronous Work Asynchronous + 24/7 Agents
Intake Manual triage during business hours Always-on queue; auto-classification; throttled execution
Coordination Meetings, real-time handoffs Queue states + SLAs + scheduled releases to humans
Failure Firefighting; unclear ownership Idempotent retries; dead-letter; on-call rotation
Cost Bursty overtime Per-task budgets; off-peak scheduling; rate limits
 

Key Design Principles for 24/7 Agents

  • Queue-First: Everything enters a durable queue with type, priority, SLA, and budget caps.
  • Idempotency: Every task carries a stable key; duplicate deliveries must produce the same outcome.
  • Retry with Backoff: Bounded retries with exponential backoff and a dead-letter path.
  • Rate Limiting: Protect upstream APIs and your wallet with global, per-route, and per-tenant limits.
  • Circuit Breakers: Trip on error spikes or latency to fail fast and prevent cascades.
  • Stateful Transparency: Persist agent decisions, prompts, and outputs for audit and reprocessing.
  • SLO-Driven Operations: Track success ratio, P95 latency, MTTR; set escalation windows that match business risk.
  • Human-in-the-Loop: Design approval and exception paths in advance—not after failures.
 

Reference Architecture (Event-Driven)

A pragmatic 24/7 stack: Event intake → Queue → Agent runners → State store → Notification & Escalation → Observability. Small, composable workers with explicit contracts (schemas) scale and rollback safely.

  1. Event Intake: webhooks, scheduled jobs, inbox/watchers (email, CRM, drive), crawlers.
  2. Queue: priorities (P0–P3), TTL, visibility timeout, dead-letter routing, per-task budget.
  3. Agent Runners: stateless workers pulling from queues; concurrency tuned to rate limits.
  4. State Store: append-only logs of inputs/outputs; vector memory only where justified.
  5. Notification: chat/email for approvals and on-call; paging only when SLAs are breached.
  6. Observability: task-level metrics, traces, and cost telemetry.
 

Agent Roles & Human Handoffs

  • Night-Shift Bot: handles intake, scoring, enrichment, and safe replies; defers risky actions.
  • Approver: receives a morning batch of “review-required” items.
  • Resolver: handles exceptions that break the normal flow.
  • On-Call: rotates weekly; only paged when P0/P1 SLAs are about to breach.

Handoff Rules (example): if queue depth > threshold for 15 min or an item overshoots its SLA by 50%, auto-page on-call; otherwise send batch summary at 09:00 local.

 

Playbooks: Five High-Impact Scenarios

  1. Overnight Lead Engine: capture → dedupe → enrich (firmographics) → score → draft reply → schedule human follow-up. What’s safe at night? Draft emails and CRM notes; defer pricing or commitments.
  2. 24/7 Support Triage: classify → answer with grounded snippets → flag risky intents → escalate with full context. What’s safe at night? FAQ-level answers; defer refunds/comp credits to approvers.
  3. Daily Exec Brief: crawl sources → summarize changes → extract KPIs → generate deck → deliver 07:30. What’s safe at night? Summaries and charts; defer decisions or announcements.
  4. Billing & Reconciliation: match payments → detect anomalies → create dispute drafts → route to finance inbox. What’s safe at night? Draft disputes; defer account holds or charges.
  5. Monitoring & Posture Mgmt: watch policy/config drift → suggest remediations → open tickets. What’s safe at night? Ticket creation; defer destructive changes.
 

Quality, Safety & Compliance

  • Guardrails: policy-aware prompts; tool allowlist/denylist; sandboxed connectors.
  • Approval Gates: human approval for irreversible actions (charges, deletions, public commitments).
  • Data Minimization: pass only required fields; mask PII in logs.
  • Provenance: store source titles or document hashes alongside outputs.
  • Test Artifacts: golden prompts, seed datasets, regression suites; measure answerability and factuality.
 

Cost & Observability (SRE for Agents)

What to measure (weekly SLOs):

Metric Why It Matters Target/SLO Example
Success Rate Tasks completed, not just attempted ≥ 98% (weekly)
P95 Latency Detect slow queues/model timeouts < 10s (P2), < 60s (P3)
MTTR Recovery speed after failure < 30 min (P1), < 4 h (P2)
Cost per Task Budget guardrail and optimization compass ≤ $0.03 per task (example)
  • Budget Guardrails: per-task & per-queue caps; auto-degrade to cheaper models; pause non-critical jobs.
  • Off-Peak Scheduling: shift heavy batch jobs to night hours relative to your main region.
  • Sampling & Caching: cache deterministic steps; sample expensive enrichments.
 

Implementation Checklist

  • Define task types, schemas, acceptance criteria.
  • Pick queues with priorities, TTL, dead-letter, visibility timeouts.
  • Make every worker idempotent; assign deterministic keys.
  • Set global + per-route rate limits; implement circuit breakers.
  • Design human approvals for irreversible actions.
  • Instrument success, latency, MTTR, and cost per task.
  • Write runbooks for retries, replays, rollbacks.
  • Pilot one playbook end-to-end before scaling.
 

Common Failure Modes & How to Recover

Failure Mode Symptom Recovery Pattern
Duplicate Execution Double emails or charges Idempotency keys; version checks; last-write-wins
Stuck Queue Growing backlog; timeouts Visibility timeouts; re-enqueue; autoscale; circuit-break upstream
API Flapping Intermittent 5xx spikes Exponential backoff; jitter; fallback models/tools
Auth/Token Expiry 401/403 bursts; sudden failures at midnight Proactive refresh; staggered rotations; secret health checks
Data Inconsistency Partial updates; mismatched totals Two-phase writes; compensating actions; replay with idempotent handlers
 

Limits: When Not to Automate

  • Irreversible actions with high legal/brand risk and low volume: keep human-first.
  • Ambiguous tasks with poor ground truth or fragmented data.
  • Processes that change weekly: document and stabilize before automating.
 

Templates & Snippets

1) Escalation Policy (JSON)

{
  "queues": {
    "p0": {"sla_minutes": 15, "page_oncall": true, "batch_summary": "09:00"},
    "p1": {"sla_minutes": 60, "page_oncall": true, "batch_summary": "09:00"},
    "p2": {"sla_minutes": 240, "page_oncall": false, "batch_summary": "09:00"}
  },
  "triggers": [
    {"if": "queue_depth > 200 for 15m", "then": "page:oncall"},
    {"if": "item_wait > 1.5 * sla", "then": "page:oncall"},
    {"if": "cost_per_task > $0.05", "then": "pause:non_critical"}
  ]
}

2) Idempotent Request (Header)

Idempotency-Key: lead-{{lead_id}}-{{yyyymmdd}}

3) Rate Limiter (Pseudo)

allow = tokens.take(tenant_id, route, "5 per second")
if !allow: enqueue({"retry_in_ms": random(200, 600)})

4) Guardrail Prompt (Approval Gate)

System: You may propose actions but must NOT execute irreversible steps.
If an action involves charges, deletions, or customer-visible commitments,
produce a review card instead of performing the action:
- Title, risk rating, evidence, reversible alternative, required approver.

5) Morning Batch Summary (Email Template)

Subject: Overnight Queue Summary ({{date}})
- New: {{count_new}} | Done: {{count_done}} | Exceptions: {{count_ex}}
- Breaches: {{p0_breaches}} P0, {{p1_breaches}} P1
- Action: Approve {{count_review}} items & assign {{count_ex}} exceptions
 

Sources & Further Reading

- Google Site Reliability Engineering (Beyer et al., O’Reilly, 2016) — SLO/SLI, error budgets, incident response

- The SRE Workbook (Beyer et al., O’Reilly, 2019) — practical runbooks, incident ops

- AWS — Exponential backoff & jitter; Dead-letter queues; Event-driven patterns

- Stripe — Idempotent requests guidance (payments-safe retries)

- Azure Architecture Center — Circuit Breaker pattern; Queue-based load leveling

 

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