Literature and research intelligence
Help agents discover relevant work, screen sources, prepare structured literature packages, identify research gaps, and preserve links back to source material.
APPLICATIONS / WHAT YOUR AGENT CAN DO
One philosophy.
Many professional tasks.
These examples show the kinds of work a professionally equipped Agent can support. Each application is assembled from the methods, tools, data, and controls appropriate to the user's task—not assumed to be a ready-made universal product. The labels distinguish existing assets, method capabilities, development-stage frameworks, and prior project experience.
FROM RESEARCH TO TRANSLATION
Equip an Agent for the part of the journey you need.
A user may start with one specialist capability and connect it to other tasks later. The examples below are application patterns, not claims that every workflow is already packaged or production-ready.
Computational chemistry and simulation
Equip agents to prepare structures and inputs, select methods with documented criteria, operate supported tools, analyse outputs, and report limitations.
Materials and engineering processes
Support workflows that may combine atomistic models, machine-learning potentials, phase-field methods, computational fluid dynamics, and multiphysics simulation where justified by the problem.
Data-driven design and optimisation
Help agents prepare datasets, engineer features, train or evaluate models, compare alternatives, and organise evidence for design or process decisions.
Manuscripts, patents, and technical outputs
Support evidence-linked writing, figure and reference checks, structured technical descriptions, patent-preparation materials, and review workflows under human approval.
Production-line intelligence
Equip agents to analyse production data, detect abnormal behaviour, support diagnosis, consider scheduling constraints, and prepare actionable recommendations for engineers.
APPLICATION IS NOT THE SAME AS PRODUCT READINESS
Start from the task. Then define what must be built.
The examples above describe where Agent enablement can be applied. A specific deployment still requires a defined data boundary, compatible tools, method selection, validation, permissions, and an agreed level of human oversight.
Check the actual requirements
Clarify the target task, user environment, available data, software licences, infrastructure, and operational constraints.
Assemble the needed capability
Choose or develop knowledge resources, Skills, tools, workflow steps, and quality checks for that particular use case.
Test against real examples
Evaluate outputs against known cases, expert review, reproducibility expectations, and agreed acceptance criteria.
Expand only when it works
Connect additional tasks or scales when dependencies and evidence support the extension.
HAVE A TASK IN MIND?Let's define what your Agent
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