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Agentic Workflows

Agentic AI makes workflow architecture a leadership issue

Agentic AI shifts the leadership question from which AI tool to buy to which decisions, workflows, data and permissions should be encoded into software.

Published
  • Software Architecture
  • Applied AI

Agentic AI changes the leadership question.

The question is no longer only: “Which AI tool should we buy?”

The better question is: “Which decisions, workflows, data and permissions should be encoded into software — and where must humans remain in control?”

That is why agentic AI is not only an AI topic. It is a workflow architecture topic.

From assistant to workflow participant

Many generative AI tools behave like assistants. A person gives an instruction, receives an answer and decides what to do next.

Agentic AI goes further. An agent may pursue a goal through multiple steps, call tools, retrieve data, use APIs, update systems, prepare decisions or trigger actions. This moves AI closer to operational workflows.

The more an AI system can act, the more the organisation must decide how work should be structured.

Architecture becomes organisational design

A workflow encodes assumptions about the business:

  • who is allowed to see information;
  • who can approve an action;
  • which data source is trusted;
  • what counts as an exception;
  • when a case should escalate;
  • which risks require human judgement;
  • what should be measured;
  • who owns the result.

When an AI agent enters that workflow, those assumptions become more important. The agent will operate according to the architecture around it: the tools it can call, the data it can access, the rules it follows, the logs it creates and the approval thresholds it must respect.

If the architecture is unclear, the agent magnifies the uncertainty.

Leadership decisions agents force into the open

Agentic AI requires leadership to make decisions that may previously have been informal.

What may AI observe?

An AI system may need access to documents, tickets, customer records, product data, financial data, code, policies or communication history. The organisation must decide what it can read and under which conditions.

Observation is not neutral. Access creates responsibility.

What may AI recommend?

Recommendation is lower risk than action, but still important. A system that recommends credit decisions, support prioritisation, compliance actions or customer responses can influence outcomes even if a human clicks the final button.

Recommendations should be explainable enough for humans to challenge.

What may AI prepare?

Preparation is often a good first production use case. AI can draft a response, prepare a case summary, classify a document, suggest an approval note or assemble a report.

Humans remain responsible for final judgement.

What may AI execute with approval?

Some workflows can allow AI to prepare an action and execute it only after approval. This pattern works well where speed matters but risk still requires a human checkpoint.

What may AI execute autonomously?

Autonomous execution should be narrow, monitored and reversible where possible. Low-risk, high-volume tasks may be appropriate. High-impact decisions usually are not.

What should AI never decide alone?

Some decisions should stay human-led: strategic trade-offs, employment decisions, sensitive customer relationships, ethical judgement, ambiguous exceptions and actions with significant legal, financial or reputational impact.

The answer will vary by company, but the question must be asked.

Human judgement becomes more important, not less

Agentic AI can reduce manual work, but it does not remove leadership responsibility.

Humans remain essential for prioritisation, context, ethics, customer relationships, trade-offs and situations where the right answer is not contained in the data. AI can support decision preparation, but it cannot own accountability for business judgement.

CBS research on workplace AI points to a related organisational challenge: AI adoption is uneven, many employees lack training and companies often have not made AI concrete and meaningful in daily work. This is a leadership issue as much as a technology issue.

New roles around AI workflows

As agentic workflows mature, companies may need clearer roles around:

  • designing workflow rules;
  • maintaining AI prompts and retrieval sources;
  • reviewing agent outputs;
  • monitoring cost and usage;
  • managing permissions;
  • handling exceptions;
  • documenting decision logic;
  • evaluating performance;
  • retiring agents that no longer create value.

In smaller companies, these responsibilities may sit with the same people who own software, operations and process improvement. The point is not to create bureaucracy. The point is to make ownership explicit.

Governance should be practical

AI governance can sound abstract. For agentic workflows, it should be practical and operational.

NIST’s AI Risk Management Framework organises AI risk work around Govern, Map, Measure and Manage. That structure is useful because it treats AI risk management as an ongoing process rather than a one-time checklist.

For smaller and mid-sized companies, practical governance may mean:

  • naming the workflow owner;
  • mapping data and system access;
  • setting approval thresholds;
  • logging tool calls and actions;
  • monitoring cost and quality;
  • reviewing permissions;
  • defining escalation paths;
  • periodically reassessing whether the agent should continue running.

This is workflow governance, not paperwork for its own sake.

Why the CIO or technical lead becomes central

The CIO, CTO or technical lead becomes central because agentic AI depends on architecture.

Business leaders may define the outcome. Operations may define the workflow. Legal or compliance may define constraints. But the technical architecture determines what the agent can actually read, prepare, trigger, log and stop.

That gives technology leadership a more strategic role: translating business intent into safe, maintainable system design.

A practical design model

A useful model is:

Business decision → Workflow rule → Data access → AI support → Human checkpoint → System action → Monitoring

This forces the organisation to connect strategy, process, data and operation.

It also prevents agentic AI from becoming a vague layer of automation with unclear accountability.

Common mistakes

Companies should avoid:

  • giving agents broad access before defining the workflow;
  • automating ambiguous decisions too early;
  • skipping human review for high-impact actions;
  • failing to log tool calls and outputs;
  • ignoring cost and token consumption;
  • treating agent setup as a one-off configuration;
  • assuming vendor AI features solve internal process ownership;
  • launching agents without a shutdown mechanism.

Gartner’s warning that many agentic AI projects may be cancelled because of cost, unclear value or weak controls is a useful reminder: the problem is often not the model alone. It is the missing operating model around it.

The practical takeaway

Agentic AI makes workflow architecture a leadership issue because it embeds decisions, permissions and actions into systems.

The companies that benefit will not simply be the ones that buy the most advanced AI tools. They will be the ones that understand their workflows, connect their systems, define decision rights, keep humans in control where judgement matters and operate AI as part of maintainable software.

Sources and inspiration

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