AI agents for business
AI agents for business that
complete the workflow.
Our agents use your company context, decide what happens next and take multi-step actions across connected tools. You choose where they can act independently and where a human must approve.
Or watch an agent work first. No form.
- Runtime decisions inside defined workflows
- 3,000+ integrations plus custom APIs
- Shared context from the Company Brain
- Configurable human approval
Xero 214 matched
Jira 38 triaged
Slack 1 handoverCompanies already building with Growy
What it covers
Explore AI agents by team, governance and workflow.
Before you hand an agent real work, you want three answers: what it can do, how far it goes on its own, and how it fits the systems you already run. Start wherever your bottleneck is: finance, day-to-day operations, or the controls that keep a production agent accountable.
Deep dive · live page
AI agents by department
See how AI agents for Finance coordinate defined work across systems while keeping approvals and material decisions traceable. The same execution model can support HR, sales and operations.
Deep dive · governance
Governed AI agents
Review permissions, human oversight and deployment controls when agents work across several systems. Growy's AI governance approach keeps those boundaries explicit.
Trust page · human-in-the-loop
AI agents in a real workflow
See how an operations reporting agent combines triggers, context, tool calls and a controlled outcome across a recurring business process.
Deep dive · by sector
AI agent use cases
Same execution model, very different jobs. Thirty-five manual steps per hire became six workflows under the recruitment agent. Against a deadline nobody has time for, the procurement agent keeps chasing until a supplier answers. Competitor pricing is swept overnight by the price intelligence agent, so the category team starts the day with movements rather than a spreadsheet.
Problem → Solution
Where AI agents for business add value beyond answers and fixed workflows.
An assistant drafts, summarises and answers. A standard workflow runs the same sequence every time. Our agents do what neither can: read the situation in front of them at runtime and decide what happens next, choosing which tool to call, which branch to take, how to classify the case, and when to stop and put it in front of you. That decision is the line between software that helps and software that finishes the job.
Before
Answers or fixed sequences
After
Context-aware execution
Interactive demo
See what AI agents can execute in production.
These are multi-step agent workflows we already have running operational work. The exact systems, time saved and success metrics depend on each customer's process and baseline.
Live agent
Onboarding agent
“Run the new-hire onboarding sequence for the next employee.”
- Read the onboarding trigger and employee contextUse the initial payload and relevant company proceduresContextQueued
- Prepare documentation and required accessCoordinate the repeatable setup steps across connected toolsToolsQueued
- Assign learning paths and onboarding materialsUse the workflow instructions and Company Brain contextKnowledgeQueued
- Pause where an approval is requiredA named approver reviews the relevant context before the path continuesApprovalQueued
- Run check-ins and complete the workflowContinue the defined sequence and retain the execution historyWorkflowQueued
Context
Running on your Company Brain: company onboarding procedures, role information and the outputs from earlier workflow steps.
Tools touched
Seen enough? Bring us one workflow.
How it works
How Growy AI agents execute a business workflow.
Classic automation breaks the moment reality stops matching the flowchart. Ours does not. The workflow stays defined and testable, but inside it the agent reads what is actually there and picks the path. Each run fires from a trigger, gathers context as steps complete, reasons at the agent nodes, calls your connected tools, and follows the branch the case deserves, not the one someone guessed at build time.
Step 01
Start with a trigger and assemble context.
A workflow can start manually, from a user chat message, on a schedule, from an integration event, through an external webhook or when another workflow completes. We support multi-trigger workflows, so several signals can start the same run. The trigger sends an initial data payload to the run. As each node finishes, its output becomes available to later steps. By default an agent can receive a summary of upstream outputs, while workflows that need exact identifiers, fields or numbers can pass the full structured output. That means the agent can reason from what the process has already learned instead of repeatedly retrieving the same information.
SlackBBusiness applicationsContext can combine company knowledge, live system data and prior step outputs
Step 02
Make a runtime decision and call the right tool.
The agent node receives a role and instructions that define what it should evaluate, decide and do. It reads the available context and chooses the appropriate action or path based on what it finds. Production workflows are mapped and approved before deployment, so this is not an agent inventing an entirely new process on every run. The flexibility sits inside the defined workflow: classify the case, choose a branch, select a tool, rank options or decide whether a condition is met. When an external action is needed, the agent calls a connected tool and checks the tool response. We connect 3,000+ integrations, including systems such as Gmail, HubSpot, Slack, Stripe, Salesforce, Notion and Zendesk, plus custom API tools for software that is not covered natively.
- Receive the trigger and current context
- Evaluate the case and choose the next action
- Call the connected tool and confirm the responseRunning
- Continue, wait or escalate according to the workflowQueued
Step 03
Pause for human judgment where the builder requires it.
Human approval is a workflow design choice, not an automatic platform rule. Builders place approval nodes at the decisions where an error would be costly, sensitive or difficult to reverse. Typical examples include financial authorisation, external communications, deletions, legal or compliance decisions and commitments made on behalf of the company. The approval node can name approvers and use a policy such as any one person, a specific number of people or everyone in the group. A rejection can stop that path. Low-risk decisions before and after the checkpoint can continue autonomously.
Checkpoint reached
Example approval checkpoint
- Agent has completed the low-risk preparation steps
- Relevant context is available to the approver
- ⚠A human decision is required before the next sensitive action
How we build your agent
How to build AI agents for business around a real operational process.
We start where it hurts, not with a blank slate and a mandate to add AI. We map the workflow, test it against your real data in a sandbox, then take it live with monitoring and a human in the loop wherever you want one.
Step 01
Define the bottleneck
Pick one job that is costing you time or breaking under volume. Onboarding and sick cover, covered by HR automation, are a common starting point because the volume is already counted. Ours began with a hire that took thirty-five manual steps and became the recruitment agent. Write down today's baseline before anything is built, so the first deployment is measurable.
Step 02
Map the workflow and decision boundaries
We map the systems the agent touches, the knowledge it draws on, the common path, the edge cases and the calls that stay yours. The Company Brain supplies your SOPs, policies and live business context, so the agent never works from generic model knowledge.
Step 03
Build and test in a sandbox
The workflow runs against your real data without taking live actions. You see what the agent decides alone, which tools it calls, how the branches behave and what reaches an approver. Plan Mode can wire the first graph from a plain description.
Step 04
Deploy, monitor and improve
It goes live with monitoring and human oversight: standing views for runs, failures and execution times, plus workflow and node-level logs. Edge cases found in production feed the next version. Judge it against your baseline, not a vendor benchmark.
Reviews
Measured evidence from three real deployments.
Three different businesses, each measured on its own processes and its own baseline. We would rather show you three programmes in depth than a page of averages, and none of these numbers is a platform benchmark.
Capacity released
20.1 full-time roles' worth of capacity released
Measured at process level rather than task level, and with the same headcount on the payroll. These are the customer's figures for their own workload, not a standard promise.
Accounting load
80–85% of accounting operations run automatically
A five-person team handled bank reconciliation, vouchers, bookkeeping and payment chasing by hand for a high-volume daily retail business. Discrepancies now route themselves to a named owner.
Quote turnaround
Quotes went from 48 hours to minutes
Sixty-plus quotation requests a day against a team that could not keep up, and no additional resource to be found. Turnaround dropped to minutes, and the business stopped losing clients and market share to the wait.
Case study
35+ manual steps per hire rebuilt as six workflows.
This is the deployment we point to when someone asks whether an agent can own a whole process instead of a task. A group of more than thirty companies, one central HR team, recruiting end to end — and trained only on the group's own decisions, which is what keeps it inside the EU AI Act.
“Recruiting took 35+ manual steps per hire, across more than thirty companies served by one central HR team. Rebuilt as six workflows on a single source of truth, with the agent trained only on the group's own procedures and past decisions — no external data, so screening stays inside the EU AI Act and the GDPR.”
Challenge
One HR team served more than thirty companies, each with its own procedures, and recruiting alone took 35+ manual steps per hire. Vacancy details were scattered across emails and chats with no single record behind them, requirements drifted between what was asked for, what was published and what was screened, and candidate sources were tagged inconsistently. CVs were reviewed one at a time against job descriptions that had already gone stale, so the outcome varied with whoever was screening that day.
Solution
Twenty of those manual steps were redesigned into six agentic workflows, with SharePoint as the single source of truth for every vacancy and every candidate record. The paper, the double entry and the manual chasing came out; the hiring decision stayed with a person. The agent was trained only on the group's own best practices, procedures and historical decisions — no external data — so screening cannot inherit bias from a public dataset, which is what makes the workflow defensible under the EU AI Act and the GDPR.
Results
35+ to 6
manual steps per hire became six workflows
75%+
of the group's HR and recruiting workflows agentic
Time to hire fell from months to a fraction of that, and the delay was never the interviewing — it was the coordination around it. Those figures are the group's own, for their processes and their volumes, and we would size your project against your own baseline.
By function and by sector
Where agents actually run inside a mid-sized company.
The execution model is the same everywhere. What changes is the volume, the systems involved and the decision you would never hand over.
Inside the business
The first agents usually go where the volume is already counted and the rules are already written down.
Hiring, leave, cover and the paperwork around a new joiner are high-volume, rule-heavy and constantly interrupted. That combination is what AI agents for HR are built for: the transactional half runs on its own, and anything touching pay or a contract stops for a named approver.
Cross-system coordination is where most operational time disappears, with a status in one tool, an approval in another and a spreadsheet holding the two together. Moving that state into the workflow rather than somebody's head is the practical case for AI agents for operations.
Service desks carry a long tail of access requests, resets and provisioning that follows a written policy every time. Teams deploying AI agents for IT teams usually start with that tail, resolving it against their own runbooks and escalating only what is genuinely unusual.
Buying, making and moving
Supply-side work is mostly waiting on other people, which is exactly the part a workflow can hold open without forgetting.
Sourcing is largely chasing: a requirement, a shortlist of suppliers and a week of follow-up before anyone replies. That loop is what AI agents for procurement run continuously, leaving the commitment itself to a buyer.
Shipments, exceptions and the paperwork behind them cross carriers, customers and internal systems. A delivery's state has to survive all three, which is the problem AI agents for logistics and supply chain are shaped around.
Production plans move against orders, stock and supplier lead times, and reconciling those views is still often manual. Keeping them aligned, and flagging a gap before it reaches the line, is where AI agents for manufacturing earn their place.
Service delivery
Where the product is people's time, the administrative half of the work is the part worth giving away.
Billable hours are the constraint, so intake, document assembly and the follow-up around a matter are what eat margin. Firms evaluating AI agents for professional services are usually trying to give that half away and keep the advice.
Customer-facing teams need approved information retrieved quickly, requests coordinated across systems and a clear line where a person takes over. Handling the routine volume while escalating anything that needs judgment is what AI agents for customer service are built to do.
Multi-site operations
Running the same procedure in fifty places is a different problem from running it once, and it is where company knowledge stops being optional.
Store networks apply the same procedures across dozens of sites, with staff who cannot stop mid-shift to go looking for them. Answering from the procedures head office already maintains, scoped per store and per role, is the point of AI agents for retail and franchise operations.
Demand swings by season and rotas are rebuilt constantly, which makes shift and cover work the first thing to break. Operators turn to AI agents for hospitality to absorb that churn without losing the approval chain behind it.
More about AI agents
A practical guide to AI agents for business.
Definitions, capabilities, use cases, platform evaluation, pricing and deployment choices for teams comparing business AI agent tools.
What are AI agents for business?
AI agents are software that decides and acts, not just software that responds. A fixed workflow repeats a sequence; an assistant waits to be asked. An agent reads the context in front of it at runtime and decides what happens next inside a defined job: classifying a request, choosing a branch, selecting a tool, scoring an item, checking whether a condition holds. Our threshold is simple: it is an agent when it can make a decision that changes the next step. That does not mean unlimited autonomy. You define the role, the instructions, the tools it can reach, the decision points and the approvals. The software takes the operational calls; you keep the ones that are sensitive, costly or hard to undo.
How do AI agents work?
A production agent needs more than a language model: a trigger, your business context, tools that can act, and a workflow that controls what happens next. That architecture is taken apart node by node under how AI agents work. A run starts manually, from chat, on a schedule, from an integration event, a webhook or another workflow finishing. What comes in and what each step returns becomes the run's context. Agent nodes use it to pick a tool or a branch, tool calls reach your systems through 3,000+ integrations and custom APIs, conditional nodes split the path, wait nodes pause, and approval nodes hold anything sensitive until your named approver responds. Every path ends at a complete node, the history is kept, memory is configurable, and your data never trains models for anyone else.
Where AI agents for business pay off
Agents pay off where work repeats, decisions depend on context, and finishing means crossing several systems. Our production examples sit in onboarding, sales operations, operations reporting and HR. In onboarding, the onboarding agent coordinates documentation, learning paths, system access and check-ins, then follows up on whatever has not landed. Sick cover and rota changes go to the HR operations agent, with approval kept where judgment is needed. Traceability is the test in finance, and the finance reconciliation agent is the case: the clean majority posts itself and only genuine mismatches reach a person. Recurring cross-system reporting, the kind that used to start with somebody opening four dashboards, is handled by the operations reporting agent. Around it sits the sourcing work, where the procurement agent chases suppliers against a deadline nobody has time for. Start with a bottleneck frequent enough to measure, and record your manual hours, cost per operation, volume and escalations first. If every step is genuinely fixed, plain automation is simpler. Agents earn their place when decisions, tool calls and company context are all part of finishing the job.
How much autonomy should AI agents have?
Match autonomy to the risk of the decision, not to how much you could automate. Our agents act alone when the logic is clear, the risk is low and the outcome is reversible: classification, extraction, branch selection, tool choice, ranking, condition checks. Anything costly or hard to undo waits for a person: payments, external communications, deletions, legal and compliance calls. You place every approval node yourself. Access is the other half of it. Agents use authenticated integrations and inherit the role-based permissions of the systems they touch, so an agent never reaches further than the person it acts for. Memory has a configurable retention period, and your data never trains our models for other customers. Autonomy is a design choice inside your workflow, not a switch on our platform.
How to evaluate AI agent platforms
The right platform depends on your workflows, systems, security model and how much control you want. Test execution, not model names: can it finish a multi-step job, or does it stop at text? Can agents reach the knowledge and the live data the job needs? Look at integrations, custom APIs, conditional logic, wait states, human approvals, logs, role-based access and how you test before production. Then run one representative workflow through every vendor under identical conditions, and watch what happens when information is missing, a tool call fails or someone rejects an action. Weigh price against implementation effort and real execution volume, and ask for customer-specific evidence rather than generic success rates.
How to build and deploy AI agents with Growy
We start with one operational job. Define the bottleneck and the outcome, map the systems, the knowledge required, the common and exceptional paths, and the decisions that stay yours. We build and test against your real data in a sandbox, covering tool calls, branches, approvals and termination paths, before anything goes live, then monitor real runs and fold the edge cases back in. The Company Brain gives your agents shared access to documented knowledge and live system context, within the permissions they are allowed. You build and change workflows in the node builder, and Plan Mode can draft the graph from a description. Running several specialised agents? An orchestrator routes work between them on the same context and the same approval rules.
Explore the Company Brain →FAQ
Questions, answered.
Common questions from teams comparing AI agent tools, implementation models and production controls.
What is the difference between an AI agent, an assistant and a standard workflow?
Do we need a technical team to build AI agents?
How much do AI agents cost?
Which tools can Growy AI agents use?
How do you measure whether an AI agent is reliable?
Get started
Choose one workflow and test an agent on it.
Start with the operational job that has a clear baseline, known systems and a real backlog. Map it, define the approval points and test the workflow before production.
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?
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