About AutomatonsX
AutomatonsX combines technology products and services across multiple business segments. Its work spans enterprise automation, SAP, data and industry-specific workflows, including regulated and non-regulated environments.
Amotion AI worked with AutomatonsX to bring agentic development practices into its teams and help them build custom agents for different product and customer requirements. The engagement connected changes in how software is developed with practical work on what those teams could build.
One approach, different delivery needs
For a company working across products and services, AI adoption needs room for different use cases. A document-processing workflow, a pharmaceutical QA process and an agent supporting on-premises operations each involve different data, decisions and constraints.
The goal was to establish a shared approach to the agentic development lifecycle (ADLC), while helping teams adapt it to their own work. This included learning to use AI in engineering, organising delivery through Linear and developing agents suited to specific business processes.
Learning through the development lifecycle
Amotion trained the team in ADLC and the practical use of Claude Code. Sessions connected AI-assisted development with planning, implementation, review and QA, keeping engineers responsible for understanding the requirement and assessing the result.
Linear training supported the workflow around that engineering work: organising tasks, making progress visible and connecting the people responsible for delivery. Together, the tool and process training gave teams a common basis for applying AI across the lifecycle.
The work also covered how to customise agent behaviour for different products and use cases. Training and guided builds were accompanied by production work on selected use cases, allowing learning and delivery to inform one another.
Agent development grounded in real use cases
AutomatonsX brought the product knowledge and business context. Amotion provided training and guidance on applying agentic approaches within that context. The work covered several related areas.
Language, documents and enterprise data
Teams worked on approaches involving natural language processing (NLP), SQL, document processing and data management. The scope included structured, semi-structured and unstructured data, with customisation for the needs of different products and workflows.
Pharmaceutical QA and human review
Training and guided work covered pharmaceutical and regulatory QA scenarios, including custom agents and human-in-the-loop systems. The emphasis was on supporting the process while retaining human review and decision-making at the relevant points.
RPA and SAP workflows
The team explored how to build and customise agents for robotic process automation (RPA) and SAP scenarios. These sessions connected agent development with the enterprise processes and domain requirements already central to AutomatonsX's work.
On-premises and long-running agents
The engagement also covered on-premises solutions for DevOps agents, alongside long-running and ambient agents for data processing and data management. These areas broadened the team's practical work beyond individual coding tasks to agents operating within wider workflows.
Building capability within the team
The work combined training, guided implementation and support for applying the learning. AutomatonsX's teams brought their knowledge of the products, customers and operating environments; Amotion helped them connect that knowledge with ADLC practices and custom-agent development.
Human involvement was part of that approach. Engineers and domain specialists remained central to directing the work, reviewing agent output and deciding how an approach should fit the use case.
Reach across the organisation
The engagement supported work across more than 20 repositories and teams involving more than 100 people, spanning multiple products, service scenarios and regulated and non-regulated sectors.
- ADLC, Claude Code and Linear training connected engineering practice with delivery workflows.
- Custom-agent work covered language, documents, enterprise data, QA, RPA, SAP and operational scenarios.
- Training and guided builds sat alongside production work on selected use cases.
- Human review remained part of both software development and the agent workflows covered by the engagement.
A foundation for the next use case
For AutomatonsX, the engagement brought together two complementary capabilities: using AI within the software development lifecycle and building agents for the business processes its products and services support.
That combination gave teams practical experience they could carry into further work, adapting a shared approach to the requirements of each product, customer and operating environment.