SCNET · Enterprise IT · Ankara, Türkiye
Sanal Çekirdek

Deploy AI agents as governed workflows.

Agentic AI refers to AI workflows that plan and carry out multi-step work from trigger to output, connecting to enterprise systems through defined tools. Sanal Çekirdek builds these workflows to be model-agnostic, to run on the organization's own data, and to operate within the permission and approval boundaries the organization sets.

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When do you need Agentic AI?

Not every automation need calls for an agent; rule-based flows are often sufficient. Agentic AI adds value when the order and content of steps change with context.

  • Repetitive multi-step processes: data collection, validation, and record updates
  • Knowledge-driven response flows: permission-filtered answers drawn from enterprise sources, with citations
  • Cross-system orchestration: work that requires reading and controlled writing across multiple systems

Anatomy of an agent workflow

Every agent workflow is defined by five elements: trigger, steps, connected systems, approval boundary, and output. A request-response agent, for example, classifies the request, reads records and knowledge sources within its permissions, drafts a response, and hands any step above the defined threshold to a human.

  • Trigger: an incoming request, a scheduled task, or a system event
  • Steps: classification, information gathering, drafting, verification
  • Connected systems: system of record, knowledge source, communication channel
  • Approval boundary: sending and record changes route to a human by threshold
  • Output: the delivered response and a step-by-step action log

Permissions, approval, and limits

What data an agent can see and what actions it can take are inputs to the design, not afterthoughts. Permissions are scoped to the narrowest set the task requires; high-impact actions are tied to human approval.

  • Suggest-only mode: the agent takes no action and produces recommendations for approval
  • Human approval and dual control: applied to steps above the impact threshold
  • Action logs: a traceable record of who, what, and which data for every step
  • Budgets and timeouts: cost and time limits defined from the outset

Production rollout and managed services

An agent is proven under production conditions, not in a demo environment. An evaluation set is built from real cases; live behavior is monitored, and problematic changes are rolled back safely.

  • Evaluation: task-based test sets measured against acceptance criteria
  • Observability: step-level monitoring across agents, models, and tools
  • Rollback: mechanisms to halt the flow and return to the previous version
  • Release management: controlled deployment of prompt, tool, and model changes

How we work

  1. We define the use case, its impact level, and the measure of success.
  2. We map the anatomy and design permission, approval, and tool contracts.
  3. We pilot the agent in suggest-only mode on real data.
  4. We expand the scope of automation gradually, based on evaluation results.
  5. We operate monitoring, action logs, and rollback in the live environment.

How success is measured

  • Task completion is measured against a defined acceptance set.
  • The rate of actions routed to human approval and post-approval corrections are tracked.
  • Cost and latency per request are reported alongside capacity planning.
  • Behavior stopped at permission boundaries is reviewed from the logs.

Frequently asked questions

How is Agentic AI different from RPA?

RPA repeats the same steps under fixed rules; Agentic AI plans the order and content of steps based on context. The two are not alternatives — an agent can call RPA as one of its tools.

Which model does an agent workflow run on?

Agent workflows are designed to be model-agnostic; the model is selected for the use case and its data requirements. Changing the model does not require rewriting the workflow.

Where should the first agent project start?

Start with a single workflow that has limited impact, measurable output, and a reversible outcome. A suggest-only pilot keeps risk low while producing real behavioral data.

What happens when an agent misbehaves?

When a defined boundary is crossed, the flow is halted, the action log is reviewed, and the previous version is restored if needed. High-impact steps already require human approval.

Related pages

Progress in agent projects starts with clear boundaries, not model horsepower. Share your scenario and we will define the anatomy and approval model together.

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