This white paper introduces the concept of Autonomous Web Entities (AWEs) — self-operating digital agents capable of interacting with web services, managing data pipelines, and making decisions within defined operational boundaries. As AI language models and browser automation technologies converge, a new class of persistent, goal-oriented web agents is emerging. We propose a formal architecture for AWEs comprising four layers: the Perception Layer (web interaction and data ingestion), the Reasoning Layer (LLM-driven decision-making with guardrails), the Execution Layer (action execution with verification), and the Memory Layer (persistent state and learning). Through prototype implementations at icrewsystems, we demonstrate AWEs handling customer support triage, automated data reconciliation, and scheduled reporting — reducing manual intervention by 87% for defined workflows. The paper also addresses safety, alignment, and the ethical considerations of deploying autonomous agents on the public web.

• H0Autonomous Web Entities cannot reliably perform defined web workflows without exceeding acceptable error rates.
• H1The four-layer AWE architecture enables autonomous completion of defined web workflows with >90% success rate.
• H2AWEs reduce manual intervention by at least 75% for structured data reconciliation workflows.

1. 01Executive Summary
2. 02Defining Autonomous Web Entities
3. 03The Four-Layer Architecture
4. 04Prototype Implementations
5. 05Case Study: Customer Support Triage
6. 06Case Study: Automated Data Reconciliation
7. 07Safety & Alignment
8. 08Limitations & Future Work
9. 09Conclusion