AI Workflow Automation
Automation framework integrating local AI models with browser automation and custom workflows to handle repetitive engineering tasks and document processing.
Overview
An automation framework that combines local language models with browser automation to execute repetitive engineering workflows, automate research, documentation, and software development tasks through intelligent decision-making.
Replaced hours of manual engineering work with intelligent automated pipelines.
The Problem
Many engineering tasks involve repetitive browser interactions and manual research. Existing automation solutions follow fixed scripts and lack contextual reasoning — they break the moment a page layout changes or a task requires judgment.
Why I Built It
To bridge AI reasoning with automation by allowing workflows to dynamically adapt based on changing user requirements and application state, making automation resilient rather than brittle.
Objectives
- Replace fixed-script automation with AI-driven adaptive workflows
- Automate research, documentation, and repetitive engineering tasks
- Build modular, reusable pipeline components
- Integrate seamlessly with the local AI infrastructure
My Role
Developed all workflow logic, integrated AI models with Playwright browser automation, built reusable automation pipeline modules, and designed the modular workflow execution architecture.
Architecture
Workflows are defined as sequences of tasks. Each task can either execute a browser action via Playwright or query a local LLM for a decision. The orchestrator chains these steps, passing context between them. Failed steps trigger retry logic with modified prompts.
Tech Stack
AI
Automation
Infrastructure
Engineering Challenges
Prompt Engineering for Reliability
Getting consistent, structured outputs from LLMs required iterative prompt refinement and output validation layers.
State Management
Complex workflows need to pass context between steps without losing information or introducing stale data.
Error Recovery
Automated pipelines must handle failures gracefully — partial retries, fallback strategies, and human escalation checkpoints.
Development Process
- 01Identified highest-ROI repetitive tasks to automate first
- 02Built basic Playwright automation primitives
- 03Integrated LLM decision layer for adaptive behavior
- 04Developed context-passing mechanism between workflow steps
- 05Added retry and error recovery logic
- 06Packaged common workflows as reusable templates
Key Features
Adaptive Workflows
Workflows adjust dynamically based on page state and LLM decisions rather than following rigid scripts.
Modular Pipeline
Each workflow step is an independent, reusable module that can be combined in different sequences.
Context Propagation
Information gathered in early steps is available throughout the entire workflow execution.
Error Recovery
Automatic retry with modified parameters, fallback strategies, and logging for failed steps.
Implementation Details
The framework uses a directed execution graph where each node is either an AI reasoning step or a browser action. Python dataclasses define workflow schemas. Playwright's async API handles concurrent browser sessions where needed.
Future Improvements
- Visual workflow builder UI
- Scheduled workflow execution with cron-style triggers
- Integration with external APIs and webhooks
- Multi-agent workflows with specialized sub-agents
Lessons Learned
AI-driven automation is only as good as the prompting strategy — vague instructions produce unpredictable results
Modularity pays off quickly — reusable components compound over time
Error handling is the majority of the real engineering work in automation systems