AI agents are most useful when they are connected to a specific workflow, given controlled access to tools or data, and placed under clear review rules. They are least useful when treated as autonomous magic. For most companies, the practical question is not “how do we become agentic?” It is “which recurring tasks could a system-connected assistant help with safely?”
What an AI agent actually changes
A standard AI assistant can answer, summarise or draft. An AI agent can go further: retrieve information, call tools, follow steps, compare sources, prepare structured outputs, update a system or route work to another queue. That extra capability is useful, but it also increases the need for boundaries.
Twoday describes AI agents as becoming core components of data intelligence platforms when they are integrated, governed and observable. CPHD Nordic takes a pragmatic view: agents can help automate workflows, but not as unsupervised autonomous systems. Humans still need a central role.
That framing is the right one for business-critical software.
Useful patterns
1. Classification and routing
An agent can read incoming requests, classify the type, identify missing information and route the case to the right queue. A human can review exceptions or low-confidence cases.
2. Document processing
An agent can extract fields from invoices, contracts, forms or reports, compare them against rules and prepare structured data for approval.
3. Knowledge retrieval
An agent can search approved internal documents, summarise relevant passages and provide a source-linked answer to employees.
4. Reporting preparation
An agent can gather data from several systems, draft a weekly report and flag anomalies for review.
5. Workflow follow-up
An agent can check whether required steps are complete, remind responsible people, or prepare the next action in a system.
These are useful because they start with existing business processes.
Where agents are risky
Agents become risky when they can take actions without meaningful constraints. Examples include sending customer communications, changing financial records, updating permissions, deleting data, accepting terms, placing orders or making decisions that require legal, financial or operational accountability.
The issue is not whether the model is “smart enough.” The issue is whether the organisation understands the action, the data, the failure mode and the person accountable for approval.
Human review is part of the design
Human review should not be added as an afterthought. It should be designed into the workflow.
Ask:
- Which actions can be fully automated?
- Which actions require approval?
- Which outputs should show confidence or evidence?
- Which cases should be escalated?
- What should reviewers be able to correct?
- How will corrections improve the workflow?
A useful design pattern is:
AI suggests → rules validate → human approves → system actsThis keeps AI inside the operational process without pretending that accountability has disappeared.
Governance and observability
Agents need more than prompts. They need:
- access control;
- approved data sources;
- tool permissions;
- audit logs;
- output review;
- fallback behaviour;
- monitoring;
- versioning of prompts and workflows;
- ownership after launch.
OWASP’s LLM risk categories are relevant here, especially excessive agency, sensitive information disclosure, insecure integrations and overreliance.
Memory(One) perspective
Memory(One) should treat AI agents as workflow components inside software systems. The right agentic workflow is narrow, useful, integrated and reviewable. It supports people by reducing repeated manual effort while keeping control where judgement and accountability matter.
Sources and inspiration
- CPHD Nordic — From workflows to AI agents: https://www.cphdnordic.com/indsigter/fra-workflows-til-ai-agenter-automatisering-der-handler-selv
- Twoday — Making AI agents first-class citizens in your data intelligence platform: https://www.twoday.com/blog/making-ai-agents-first-class-citizens-in-your-data-intelligence-platform
- OWASP — Top 10 for Large Language Model Applications: https://owasp.org/www-project-top-10-for-large-language-model-applications/
- NIST — AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework