Building Reliable Office Automation with Temporal, Local LLMs, and Robot Framework
Most automation projects fail not because AI is weak — but because workflow reliability is underestimated.
Emails arrive late.
Humans respond days later.
Systems crash.
SAP has no API.
If your automation cannot pause, retry, resume, and audit, it will eventually break.
This article explains a modern, production-ready architecture that combines:
- Temporal — durable workflow orchestration
- Local LLMs (Ollama) — intelligent routing & extraction
- Robot Framework — UI automation for systems without APIs
This stack is especially suitable for finance, ERP, procurement, and office processes.
The Problem with Typical Automation Stacks
Most teams start with:
- Cron jobs
- Message queues
- Low-code tools
- LLM agents
These work well for short tasks, but break down when workflows:
- run for hours or days
- require human approval
- involve retries and compensations
- must survive restarts and deployments
Example failures:
- Invoice posted twice after retry
- Approval lost because a worker crashed
- LLM made a confident but wrong decision
- UI automation ran out of order
What’s missing is a durable workflow engine.
The Core Idea: Separate Intelligence, Orchestration, and Execution
The key architectural principle is separation of responsibility:
| Layer | Responsibility |
|---|---|
| Temporal | Orchestration, state, retries, guarantees |
| Local LLM | Understanding & classification |
| Robot Framework | UI execution (SAP, legacy systems) |
No layer does another layer’s job.
High-Level Architecture (Text Diagram)
Email / API / Schedule
|
v
Temporal Workflow (Source of Truth)
|
+--> Activity: Local LLM (Ollama)
| - classify email
| - extract fields
| - return confidence
|
+--> Gate (confidence / rules)
|
+--> Activity: Robot Framework
| - SAP UI automation
| - legacy system interaction
|
+--> Signals
- human approval
- correctionTemporal owns what happens next, not the AI.
Why Temporal (Not Cron, Not Queues, Not n8n Alone)
Temporal is different because it provides:
- Durable execution — workflows survive crashes
- Exactly-once semantics — no duplicate invoices
- Deterministic replay — perfect audit trail
- Human-in-the-loop — wait days without hacks
- Built-in retries & timeouts
In short:
Temporal treats workflows like a database treats data.
Role of the Local LLM (Ollama)
The LLM is not the brain of the system.
It acts as an advisor.
Typical responsibilities:
- Classify incoming email (invoice, RFQ, reminder)
- Extract structured data (invoice number, amount)
- Report confidence level
- Explain uncertainty
Example LLM output:
{
"workflow_key": "invoice.receive",
"confidence": 0.82,
"extracted_fields": {
"invoice_no": "INV-2025-001",
"amount": 125000,
"vendor": "ABC SUPPLY"
}
}Important rule:
LLMs suggest. Temporal decides.
Gate Design: Where Automation Becomes Safe
Temporal enforces gate logic:
if decision.confidence < 0.8:
wait_for_human()Other gates may include:
- required fields present
- amount threshold
- vendor allowlist
- duplicate detection
This prevents:
- hallucinated actions
- silent failures
- irreversible mistakes
Robot Framework: Automating Systems Without APIs
Many enterprise systems (SAP GUI, legacy ERP) still rely on UI.
Robot Framework is ideal because:
- deterministic
- testable
- screenshot & log based
- widely supported
Temporal runs Robot Framework as an activity, meaning:
- retries are controlled
- failures are visible
- execution is isolated
If Robot crashes:
- Temporal retries or escalates
- workflow state remains intact
Example Workflow: Vendor Sends Invoice by Email
- Email arrives
- Temporal workflow starts
- LLM classifies email → invoice.receive
- Confidence = 0.91 → passes gate
- Robot Framework posts invoice in SAP UI
- SAP document number returned
- Workflow completes
- Audit trail preserved forever
If confidence were low:
- workflow pauses
- human approves or corrects
- workflow resumes automatically
Why This Stack Scales
Scaling LLM usage
- Local inference (Ollama)
- Stateless activities
- Easy model swap
Scaling automation
- Temporal workers scale horizontally
- Robot runners isolated per VM
- No shared mutable state
Scaling teams
- Developers work in Git
- Ops monitor Temporal UI
- Business sees audit logs
When NOT to Use This Stack
This architecture is not for:
- simple Zapier-style automation
- chatbot-only projects
- short-lived scripts
- one-off integrations
It is for:
- finance workflows
- ERP processes
- approvals
- compliance-sensitive automation
- systems that must never double-execute
Final Takeaway
If your automation:
- touches money
- waits for humans
- integrates with fragile systems
- must survive failure
Then:
Temporal is the backbone,
LLMs are advisors,
Robot Framework is the hands.
This separation is what turns AI automation from a demo into a reliable system.
Get in Touch with us
Chat with Us on LINE
iiitum1984
Related Posts
- Agentic Commerce：自主化采购系统的未来（2026 年完整指南）
- Agentic Commerce: The Future of Autonomous Buying Systems (Complete 2026 Guide)
- 如何在现代 SOC 中构建 Automated Decision Logic（基于 Shuffle + SOC Integrator）
- How to Build Automated Decision Logic in a Modern SOC (Using Shuffle + SOC Integrator)
- 为什么我们选择设计 SOC Integrator，而不是直接进行 Tool-to-Tool 集成
- Why We Designed a SOC Integrator Instead of Direct Tool-to-Tool Connections
- 基于 OCPP 1.6 的 EV 充电平台构建 面向仪表盘、API 与真实充电桩的实战演示指南
- Building an OCPP 1.6 Charging Platform A Practical Demo Guide for API, Dashboard, and Real EV Stations
- 软件开发技能的演进（2026）
- Skill Evolution in Software Development (2026)
- Retro Tech Revival：从经典思想到可落地的产品创意
- Retro Tech Revival: From Nostalgia to Real Product Ideas
- SmartFarm Lite — 简单易用的离线农场记录应用
- OffGridOps — 面向真实现场的离线作业管理应用
- OffGridOps — Offline‑First Field Operations for the Real World
- SmartFarm Lite — Simple, Offline-First Farm Records in Your Pocket
- 基于启发式与新闻情绪的短期价格方向评估（Python）
- Estimating Short-Term Price Direction with Heuristics and News Sentiment (Python)
- Rust vs Python：AI 与大型系统时代的编程语言选择
- Rust vs Python: Choosing the Right Tool in the AI & Systems Era
