Explore Tasks
Design and bound a tool-using agentic AI workflow
The owner receives a bounded agent design with tool permissions, evaluation and failure handling.
2jobs do it
1job helps with it
40skills it needs
Does itHelps with it
- Reading and tracing unfamiliar code
- Reviewing human- and AI-generated code changes for correctness and risk
- Sampling and reviewing a team's work against standards
- Diagnosing misleading data visualisations
- Verifying reports and dashboards before and after release
- Verifying AI-assisted outputs against sources
- Disclosing AI contribution to professional work
- Testing competing explanations with discriminating evidence
- Independently checking analytical conclusions and claims
- Escalating unresolved risks with evidence and clear ownership
- Giving specific developmental feedback on data and AI work
- Judging the materiality of defects and deficiencies for the intended use
- Designing AI-assisted workflows with bounded tools and human authority
- Bounding and securing AI systems' tool and data access
- Designing and running evaluations of AI systems
- Classifying AI uses against regulatory obligations
- Presenting and defending evidence-based recommendations to decision-makers
- Decomposing one's own tasks for AI assistance
- Protecting sensitive data in AI-assisted work
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Analysing error patterns and failure modes in models and AI systems
- Evaluating responsible AI use risks
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Rating risks against appetite and choosing proportionate treatment
- Understanding and interrogating data and application APIs
- Writing maintainable code for analytical and data work
- Building bounded, secure data-service APIs
- Designing and evaluating retrieval for AI systems
- Writing evidence-led findings and auditable recommendations with their limits
- Specifying and iterating AI task instructions
- Building data protection by design into data products and pipelines
- Gating releases on evidence with rollback ready
- Setting and reviewing action thresholds for scores and model predictions
- Training reproducible machine learning models
- Evaluating ML generalisation and fitness
- Monitoring model fitness and drift
- Assessing data-driven fairness and bias
- Setting and applying a release and rollback bar for models and AI systems
Paths that teach the skills it needs
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Responsible AI Governance and Assurance
Govern and assure AI across its lifecycle including third-party AI and redress.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonApplied AI at WorkMachine Learning: from Data to Validated Models and DecisionsDesigning Controlled Automations and AI-assisted Workflows