AI agents · How they work
How AI agents work inside a controlled business workflow.
An agent is not a chat window. See what starts a run, what it decides, where it acts, and where it stops for you.
How it works
How how AI agents work move from trigger to outcome.
See how how AI agents work turn a defined a multi-step internal request into an observable workflow that an in-house technical team can build, test and operate.
Map the how AI agents work workflow
The workflow starts when classify a request receives a defined trigger and the minimum identifiers required to find authoritative context in Company Brain. The team documents the payload, access rules and expected output before adding model reasoning.
Design inputs
Connect data and tools for how AI agents work
An agent node can then retrieve approved context using Microsoft 365 and the Company Brain. Conditions determine whether the run continues, waits for more information or asks a person to review the case.
Execution
Test, approve and monitor how AI agents work
Connected actions allow the workflow to call a connected system in CRM. Growy records the path taken, while route an exception remains explicit whenever ambiguous instructions could affect the outcome.
Controls
Interactive demo
How AI agents work workflow example
A representative controlled workflow for classifying an incoming request.
- QueuedReceive the triggerAutoCollect the request and relevant context from the Company Brain.
- QueuedPause when judgment is requiredHumanRoute ambiguous input to a named approver with the current context.
- QueuedComplete and recordAutoExecute the approved action, retain logs and measure completion rate.
From manual coordination to controlled execution
How AI agents work should remove handoffs without hiding risk.
Teams often have the systems and expertise required for cross-functional business workflows, but the work still depends on people moving context between tools and chasing the next action.
Manual or fragmented workflow
- People move information between the Company Brain and business applications.
- classifying an incoming request depends on inboxes and memory.
- ambiguous input is handled through an informal message.
- Exceptions around a multi-step internal request are handled through messages and individual judgment.
Growy agent workflow
- Connected nodes pass structured context through the run.
- The workflow starts from a defined trigger and completion state.
- A named Human Approval node pauses the exact action.
- The agent routes the exception with the relevant evidence, owner and permitted next action.
The basics
What are how AI agents work?
how AI agents work are software workflows that combine model-based reasoning with business rules, company knowledge and connected tools. They do more than generate an answer: they can evaluate context, select a route and complete approved steps across a process. Within How AI agents work, AI agents provides the broader context for this part of the workflow.
Growy supports manual, chat, scheduled, integration-event, webhook and workflow-completion triggers, and several can drive the same workflow. Plan Mode can scaffold and wire the workflow graph from a natural-language description. The workflow shape is defined and tested before production. Model reasoning is scoped to individual steps, so the agent does not freely rewrite its own process at runtime. The next logical part of this cocoon is Business process management, where the adjacent use case is developed in detail.
For a 500 to 1,500-person organisation, the practical distinction is ownership. Growy gives the platform engineering team a platform for designing and maintaining the workflow itself, with onboarding available to establish the first controlled deployment.
Core capabilities
How AI agents work: capabilities to evaluate before production.
Evaluate the complete operating model, not only the language model.
Company context for how AI agents work
Use the Company Brain to connect policies, documents and live systems while respecting source permissions. How AI agents work connects directly with Enterprise AI agents when teams define shared data, rules and ownership.
Ground cross-functional business workflows decisions in company context
Retrieve approved information from Company Brain, Microsoft 365 and the Company Brain so each node receives context relevant to its task rather than an uncontrolled collection of documents. For a complementary perspective, Company Brain shows how the same platform principles apply elsewhere.
Connect the systems behind a multi-step internal request
Use Growy integrations to read or update CRM and ticketing system. Each action can be scoped, tested and checked for a successful response before the workflow continues.
Keep consequential exceptions reviewable
Add conditions, waits and human approvals where ambiguous instructions requires judgment. Logs and route-level metrics help the process owner review what happened after deployment.
Approach comparison
Compare ways to automate cross-functional business workflows before choosing an operating model.
The right option depends on whether the goal is a single answer, a fixed task or a multi-system workflow maintained by the internal team.
Executing stable, deterministic steps for a multi-step internal request when inputs and outcomes are predictable.
Struggles when classify a request requires interpretation or when unstructured context changes the route.
Drafting, summarising and answering questions about cross-functional business workflows when a user remains in control.
Usually leaves the employee to move the result into Microsoft 365, CRM and the rest of the process.
Helping a user complete retrieve approved context inside one application with suggestions and contextual guidance.
May not coordinate call a connected system across CRM and ticketing system or preserve one auditable route end to end.
Combining company context, conditions, approvals and connected actions to operate a multi-step internal request across systems.
Requires the platform engineering team to define permissions, test exceptions and own the workflow after release.
Seen enough? Bring us one workflow.
Best-fit organisations
How AI agents work for teams ready to build internally.
how AI agents work are most useful when technical ownership and process authority can work together.
Platform engineering team
Build the graph, configure integrations, manage releases and monitor failures without depending on Growy for every workflow change.
Operational process owners
Define rules, exceptions and metrics for classifying an incoming request and validate whether the agent improves the real process.
Cross-functional business workflows process owners
Define what success means, identify exceptions and approve the rules that govern a multi-step internal request.
Business leaders
Compare completion rate, execution time, failure rate and escalation rate against the current baseline before increasing scope or autonomy.
Implementation playbook
How to build how AI agents work with Plan Mode and human oversight.
Move from a narrow use case to an operated cross-functional business workflows workflow with explicit evidence at every stage.
Choose one measurable process
Start with classifying an incoming request and document volume, manual effort, delay, exceptions and current owners.
Map the data and systems for a multi-step internal request
Identify the authoritative record in Company Brain, the context required from Microsoft 365 and the permitted action in CRM. A related implementation pattern appears in AI agents for operations, with a different operational boundary.
Configure context, tools and boundaries
Connect the Company Brain, business applications, authenticated APIs and workflow and node-level logs, scope each node and place human approval around ambiguous input, an unavailable API, a sensitive action and an unexpected edge case.
Test normal and exceptional cross-functional business workflows routes
Use representative cases, missing data, rejected approvals and simulated integration failures to verify every terminal state.
Deploy, measure and own
Release to a controlled user group, monitor successful completion rate, failure and escalation data, then let the platform engineering team revise the workflow through documented versions.
Workflow examples
How AI agents work: examples to validate with your own stack.
The workflow shape is defined and tested before production. Model reasoning is scoped to individual steps, so the agent does not freely rewrite its own process at runtime.
“Can an agent coordinate classifying an incoming request?”
The agent receives a multi-step internal request, uses Company Brain to establish context and applies a documented condition before selecting the next action.
“What happens when the workflow meets ambiguous input?”
It can retrieve approved context, prepare the result for review and only write to CRM after the required permission or approval is present.
“What happens when ambiguous instructions is detected?”
The workflow follows a named exception branch, preserves the evidence used and sends the case to the accountable owner instead of generating a plausible completion.
“How does the platform engineering team improve the agent after launch?”
Compare completion rate, execution time, failure rate and escalation rate with the organisation's baseline. Do not substitute a generic AI productivity claim for process evidence.
FAQ
How AI agents work questions answered.
Practical guidance for evaluation, build and governance.
What are how AI agents work?
What can how AI agents work automate?
Which systems can how AI agents work connect to?
Do how AI agents work replace the platform engineering team?
How should how AI agents work handle sensitive decisions?
How do you measure how AI agents work?
What should teams measure for how AI agents work?
Can a company build how AI agents work without Growy services?
Questions answered? Put it on your own workflow.
Sector proof
Proven at a 100+ store grocery retail franchise.
These are the figures measured at a 100+ store grocery retail franchise, for their processes and their volumes. Read them as evidence that the workflow runs, not as a number your deployment will reproduce.
“Sick cover is the clearest example of a decision taken at runtime. The workflow identifies who is eligible, contacts them in priority order, confirms the swap and updates the rota, with no manager chasing anyone. Five manual HR workflows had been structurally overloading a correctly sized team — rostering alone cost 100 store managers four hours a week each.”
Detailed guide
How AI agents work: strategy, architecture and rollout
How AI agents work: business process and search intent
Understanding how AI agents work starts with the event that creates a run. A manual command, a chat message, a schedule, an integration event, a webhook or the completion of another workflow can supply the first payload. Several triggers can drive the same workflow. The trigger is not merely a notification: it determines which identifiers, records and initial instructions become available downstream. Later nodes may receive a summary of earlier outputs, the complete structured result or only selected predecessors. That context policy matters because a reasoning step cannot make a reliable decision from information it never received. The execution graph establishes order before the model is asked to reason. Triggers, actions, conditions, waits, approvals and completion nodes form a reviewable route through the process. Plan Mode accelerates this design stage by translating a natural-language description into a connected graph. The generated graph is then inspected and tested by the builder. Runtime intelligence remains bounded inside agent nodes: the model can interpret, classify, extract or generate according to its instruction and output contract, but it does not silently redesign the complete workflow while a case is running. Tool use is the point at which an AI agent moves beyond advice. An authenticated integration lets a node retrieve a record, create an update, send a message or perform another documented action. Growy supports more than 3,000 available integrations as well as custom API and HTTP connections. A sound implementation checks the response returned by the external service and routes failures explicitly. Wait nodes can support delayed or event-based continuation, conditions can separate response types and ambiguous cases can be sent to a person rather than being treated as successful.
How AI agents work: data, integrations and company context
Production learning should not be confused with uncontrolled self-modification. When an unexpected case appears, the team can inspect the run, update the graph or node instructions, validate the change in a sandbox and deploy a new version of the operating logic. Customer data is not used for cross-tenant model training. Any customer-specific memory optimisation is opt-in and remains scoped to that tenant. The result is a controlled improvement cycle based on observed cases rather than an agent rewriting its own rules during execution. Operational evaluation follows the whole run. Workflow views expose runs, failures and execution times, while node-level logs help a technical team locate the step that produced an exception. Useful measures include completion, latency, escalation and failure, but no single platform-wide benchmark should replace a process baseline. The correct question is whether the agent completes the chosen business outcome more reliably and with fewer manual handoffs than the previous method. That comparison requires representative cases, explicit terminal states and a clear definition of success. The execution graph establishes order before the model is asked to reason. Triggers, actions, conditions, waits, approvals and completion nodes form a reviewable route through the process. Plan Mode accelerates this design stage by translating a natural-language description into a connected graph. The generated graph is then inspected and tested by the builder. Runtime intelligence remains bounded inside agent nodes: the model can interpret, classify, extract or generate according to its instruction and output contract, but it does not silently redesign the complete workflow while a case is running. Teams evaluating How AI agents work can also review AI agents for IT teams before fixing approval and escalation points.
How AI agents work: governance, security and human oversight
The execution graph establishes order before the model is asked to reason. Triggers, actions, conditions, waits, approvals and completion nodes form a reviewable route through the process. Plan Mode accelerates this design stage by translating a natural-language description into a connected graph. The generated graph is then inspected and tested by the builder. Runtime intelligence remains bounded inside agent nodes: the model can interpret, classify, extract or generate according to its instruction and output contract, but it does not silently redesign the complete workflow while a case is running. Understanding how AI agents work starts with the event that creates a run. A manual command, a chat message, a schedule, an integration event, a webhook or the completion of another workflow can supply the first payload. Several triggers can drive the same workflow. The trigger is not merely a notification: it determines which identifiers, records and initial instructions become available downstream. Later nodes may receive a summary of earlier outputs, the complete structured result or only selected predecessors. That context policy matters because a reasoning step cannot make a reliable decision from information it never received. Human oversight is embedded as an execution state. A Human Approval node identifies the approver, explains what needs review and applies a policy such as one named person, a defined number of approvers or everyone in the group. Rejection stops that path unless the builder has deliberately created a fallback. This gives operators a visible boundary between low-risk automation and consequential judgment. It also makes the approval part of the process record instead of leaving it in an email or side conversation that the workflow cannot interpret.
How AI agents work: implementation considerations
Most of the difficulty in a first deployment is scoping rather than modelling. Choose one process with a known trigger, a bounded set of systems and a terminal state someone can name, and resist widening it until that version runs unattended. A workflow crossing four systems and one team can reach production quickly; one crossing eleven systems and three departments becomes a project. Access is the next constraint and usually the slowest to clear. Each integration runs under a credential with its own scope, and an agent inherits exactly the permissions of the account it authenticates as, which is why the service accounts and their owners are worth agreeing before the graph is built rather than after. Testing should use a pack of real cases instead of invented ones. A useful pack pairs the routine shape of the process with the cases that broke it last quarter: missing fields, conflicting records, an integration that times out, an input nobody anticipated. Each case should assert a terminal state, whether completed, escalated or stopped, so a run that ends in the wrong place fails the test rather than passing quietly. Ownership is the last consideration and the one most often left out. Changes to instructions, thresholds and approval policies are versioned and released like any other operating change, which means a named process owner, a sandbox to validate in and a documented way back to the previous version. Deployments rarely fail on the model. They fail because nobody was responsible for the workflow once the build team moved on, and an agent without an owner degrades quietly as the process around it changes.
Get started
Build how AI agents work around one real workflow.
Give your platform engineering team a platform to design, test and operate how AI agents work, with onboarding available for the first controlled deployment.
Two ways to start
Book a demo6 FIELDSYou already know what you want to see. Tell us the essentials and we come back with times.Open the form →Build your agent6 QUESTIONSYou want the demo built around your own bottleneck. Answer six questions, we prepare the agent for the call.Start the questionnaire →Prefer a live call?
Grab a slot that suits you.
Book a 30-minute call with our team.
Opens the calendar over this page. Hosted by Calendly, which sets its own cookies.