Lots of buzzwords. Lots of tech lingo..
..but the basic idea is actually quite simple.
At a high level, an agentic AI workflow is just a handful of components working together.
Let’s demystify the lot and make it understandable in plain, simple language.
The reasoning
This is the LLM, effectively the brain of the system.
- It receives information, analyses it, works out what is happening and suggests what should happen next.
- The LLM itself does not necessarily have direct access to your servers, databases or other systems.
It reasons about the information it has been given and can request one of the actions you have made available to it.
The trigger and loop
Something has to start the process.
That might be a user request, an event, an API call, a message in a queue, a scheduled job, or something as simple as a cron job starting the agent every minute.
Once running, the system normally operates as a loop:
Collect information -> analyse it -> decide what to do -> take an action -> observe the result -> repeat if necessary.
There does not have to be a permanent “heartbeat”.
A scheduled heartbeat is simply one possible way of triggering the workflow.
The agent
The agent is the orchestration layer around the LLM.
- It collects the information the LLM needs, such as logs, metrics, application state or database records, and sends the relevant context to the model.
- The model analyses that information and may recommend an action or request a particular tool.
- The agent then checks whether that action is permitted and, if it is, calls the appropriate worker or tool.
- Afterwards it can collect the resulting logs or state, send them back to the LLM and ask:
Did that work? Is the system now in the expected state? Do we need to do anything else?
That creates the agent loop.
The workers
Workers are the components that actually perform actions.
They can be very simple scripts, services or existing applications.
For example, a worker might:
- collect logs
- query a database
- restart a service
- run a diagnostic command
- change a configuration value
- open a support ticket
- send a notification
The important part is that the workers expose a limited set of clearly defined capabilities.
The LLM does not need unrestricted shell access just because one worker is capable of restarting a service.
State and memory
Depending on the application, the agent may also maintain some state.
That could be as simple as remembering what happened during the current run, or it might include previous actions, observations, decisions and results.
This gives the LLM enough context to understand what has already happened instead of treating every step as an entirely new problem.
Keeping it bounded
The example above describes a bounded agentic workflow.
- You are not giving the LLM the keys to the kingdom and allowing it to execute whatever command it happens to generate. Instead, the LLM can reason and make decisions within the capabilities you deliberately expose to it.
- The agent controls the process.
- The workers control what can actually be done.
- Permissions control what those workers can access.
- For sensitive operations, you can add validation, policy checks or human approval before anything happens.
And that is really the important part.
Agentic AI does not have to mean giving an AI model complete autonomy over your infrastructure.
In a production environment, it can simply mean allowing an LLM to make bounded decisions inside a conventional software system, using a deliberately restricted set of tools.
Strip away the terminology and it is essentially:
Observe -> reason -> act -> verify -> repeat.
While keeping a human in the loop (HITL) is not always necessary, you’re letting a reasoning machine do the main work while still retaining human control for critical decisions where it matters or is required.
Agentic AI does not automatically mean autonomous AI with unrestricted access.
It can be tightly controlled, narrowly scoped and built around conventional software controls.
A simple example
- A monitoring system notices that an application is behaving unusually.
- The agent collects logs and metrics and sends them to the LLM.
- The LLM concludes that a service may have failed and recommends a restart.
- The agent checks whether restarting that service is an allowed action.
- A worker performs the restart.
- The agent collects new logs and metrics and asks the LLM whether the system has recovered.
- If everything looks normal, it stops. If not, it continues or escalates to a human.
Key terms
- LLM: The AI model doing the reasoning. Think of the AI behind things like ChatGPT, Grok, Claude etc.
- Agent: The part that coordinates the process and decides what happens next.
(a small application that glues the work together with the LLM) - Worker: A script, service or application that actually performs an action.
Go fetch the log, trigger the reset – this is the part that has the permissions and access to do it. - Tool: A capability the agent is allowed to use.
This could be the ability to collect a log, push the reset button, launch an app… - Trigger: Something that starts the workflow.
- Loop: The repeated cycle of observe, reason, act and verify.
- State / memory: Information about what has already happened.
(a database or similar – like Post-its on the wall) - HITL: Human in the loop, where a person approves or controls certain decisions.
- Guardrails: Rules and restrictions that limit what the agent (and ai) can do.