How we work
Business outcomes, workflow by workflow
Each workflow starts from the business result it is meant to produce - faster conversion, lower handling cost, higher capacity, fewer errors - and then shows who does what, what stays recorded, and where the automation stops.
- AI agent, with access only to what it needs
- Human decision - a person approves or takes over
- Existing systems: GitHub, Active Directory, Azure, ServiceNow, telephony, CRM/ERP
GitHub flow exampleIssue → PR → tests → review → approval → deploy
From issue to deploy, with human approval
A request becomes a pull request through agents, and stops for a human decision before anything reaches production.
GitHub
Issue
Request, incident or improvement, with acceptance criteria.
Human or agent
Team lead
Coordinator AI
Reads it, splits the work and picks the right agent.
AI agent
GitHub
Developer agent
Creates the branch, writes the code, opens the pull request.
AI agent
GitHub Actions
Tester agent
Runs the tests, the build and the checks; reports the result.
AI agent
GitHub
Reviewer agent
Reads the diff, checks the risks, comments and proposes approval.
AI agent
Team lead
Human approval
A person decides: merge or send back - the automation stops.
Human
GitHub · Infra
Merge + DevOps
Merge, deploy, monitoring. Alerts become a new issue.
AI agent · Human
Alert, incident or improvement → new issue: the cycle starts again
- The whole history is in GitHub: issue → branch → pull request → checks → review → merge.
- Nothing reaches production without a human approval.
- Each agent has access only to what its step needs.
Call centre exampleCall → identify → resolve → escalate
From call to resolution, with a human operator
Repetitive calls are handled by the voice agent; sensitive cases reach an operator with the context already prepared.
Telephony
Incoming call
The client calls the team number. The agent answers.
Client
AI agent
Voice agent
Introduces itself, listens and under- stands the request.
AI agent
CRM · ERP
Verification
Pulls the history: contract, invoices, open tickets.
AI agent
Actions
Resolution
Resolves what it can: information, booking, a new ticket.
AI agent
Escalation
Human operator
Sensitive cases reach an operator, with the context ready.
Human
CRM
Note in CRM
Everything is logged; the follow-up is scheduled for you.
AI agent
Reporting
Daily report
Calls, reasons, resolution, average time - automatic.
AI agent · Human
Patterns from calls → new rules: the cycle restarts
- Every call leaves a note in the CRM - nothing is lost.
- The operator steps in only where it matters.
- The daily report builds itself.
Sales exampleLead → qualify → quote → follow-up
From lead to quote, with a human in the conversation
Leads are qualified and followed up automatically; the salesperson joins with the whole context prepared.
E-mail · LinkedIn
New lead
A request from the form, an e-mail or a LinkedIn message.
Human
AI agent
Qualification
Checks the fit, the budget and how urgent it is.
AI agent
CRM · Documents
Quote draft
Pulls the data from the CRM and prepares the quote.
AI agent
Follow-up
Auto follow-up
Sends reminders and reschedules the conversation.
AI agent
Sales
Sales call
The salesperson takes over, with history and quote ready.
Human
Quote
Quote sent
The quote goes out, the status updates in the CRM.
AI agent
Forecast
Pipeline report
Pipeline, response rate, forecast - up to date, automatic.
AI agent · Human
Every interaction feeds the forecast and the priorities
- Every lead has a scheduled next step.
- The salesperson spends no time on qualification.
- The forecast comes from data, not from impressions.
Recruiting exampleAd → CVs → interviews → offer
From job ad to offer, with the decision left to people
Screening, scheduling and tests run automatically; the recruiter decides who is chosen.
LinkedIn · Site
Job ad
The role goes out on LinkedIn and the site, with clear criteria.
Human
AI agent
CV screening
Reads the CVs and scores them against the role criteria.
AI agent
Calendar
Scheduling
Sends the invitations and keeps the calendar in sync.
AI agent
Interview
Human interview
The recruiter decides; the agent only prepares the context.
Human
Technical test
Test sent
Sends the test, tracks the deadline and collects the answers.
AI agent
Offer
Offer and papers
Prepares the offer and the onboarding documents.
AI agent
Reporting
Hiring report
Time per stage, best sources, drop-off - automatic report.
AI agent · Human
Good sources and criteria become the priorities for the next roles
- Every candidate gets an answer, in time.
- The hiring decision stays with a person.
- The good sources show up in the data, not in guesses.
Accounting exampleInvoices → extraction → validation → ERP
From invoice to ledger, with human validation
Invoices are read and prepared automatically; the accountant validates the exceptions and approves.
E-mail · Portal
Documents
Invoices and receipts arrive by e-mail or through the portal.
Supplier
AI agent
Reading invoices
Extracts supplier, amount, VAT and the due date.
AI agent
Checking
Matching
Matches it against the orders and the existing contracts.
AI agent
Accounting
Human validation
The accountant checks the exceptions and approves.
Human
ERP
Ledger entry
The data goes into the ERP with no manual typing.
AI agent
Reconciliation
Payment match
Payments are matched against the outstanding invoices.
AI agent
Reporting
Deadlines
Due dates, diffe- rences, VAT - a report on time for the team.
AI agent · Human
Discrepancies become new validation rules
- No invoice gets lost: every one has a status.
- The accountant validates only the exceptions.
- Deadlines are visible early, not at the last moment.
Onboarding and offboarding exampleHR → Active Directory → Azure → groups → access
From hire to access, with a human approval
The hire is entered once in the HR tool; the agent prepares the account, the groups and the access, and IT approves before anything is granted.
HR tool
Hire entered
The hire is entered in the HR tool. One simple form.
Human
AI agent
Request read
Reads role, team, location and the start date.
AI agent
Active Directory
Account created
Creates the account, fills the attributes and the right unit.
AI agent
Azure
Synced and grouped
Syncs with Azure and adds the person to the needed groups.
AI agent
Access
Rights prepared
Prepares access to applications, mail and resources, by role.
AI agent
IT
Human approval
Nobody gets access without approval: IT validates first.
Human
Offboarding
Same flow reverse
On exit, the same flow in reverse: account, access cut.
AI agent · Human
The same rules apply to every hire and every exit
- Nobody gets access without a human approval.
- Offboarding is as automatic as onboarding.
- Every step is logged: who, when, which access.
Change management exampleServiceNow → plan → approval → Cloudflare → evidence
A DNS record changed through change management
A DNS record in Cloudflare is changed only through a ServiceNow change request: the agent prepares it, a person approves it, and the evidence lands back on the ticket.
ServiceNow
Change opened
A change request is opened for the DNS record.
Human
AI agent
Impact checked
Checks what changes, who is affected and what the risk is.
AI agent
DNS · Cloudflare
Zone read
Reads the zone, the current records and the TTL.
AI agent
Safety
Rollback written
States exactly how to roll back if something goes wrong.
AI agent
Change advisory
Human approval
A person approves; without approval nothing runs.
Human
Cloudflare API
Change applied
The API applies the change and verifies the result.
AI agent
ServiceNow
Ticket closed
The evidence is attached to the ticket and it closes.
AI agent
Every change stays documented, with evidence and a rollback
- Nothing changes in DNS without approval and a way back.
- The evidence stays on the ticket, not in a chat.
- The same rules cover any infrastructure change.
App development exampleIdea → plan → code → tests → approval → release
From idea to published application, with human approval
An application is built step by step by agents: plan, code on branches, automated tests; a person approves what goes live.
Backlog
Idea or request
An idea or a request is written down with what it must do.
Human
AI agent
Plan and design
Studies the request, proposes the design and splits the work.
AI agent
GitHub
Code written
Builds the app on branches: screens, data, integrations.
AI agent
GitHub Actions
Tests run
Tests, build and quality checks run on every change.
AI agent
Security
Checks pass
Dependencies, secrets and vulnerabilities are checked.
AI agent
Product owner
Human approval
A person approves the release; nothing goes live without it.
Human
Release
Published
The app is published and monitored; errors become new tasks.
AI agent · Human
Feedback from real use becomes the next version
- Every version has a record: request → branch → tests → approval.
- Nothing goes live without a human approval.
- Errors found in production become tasks, not surprises.
IT security exampleAlert → triage → action → approval → report
From alert to response, with a human decision
Alerts from the security systems are triaged and prepared by an agent; a person approves the action, and everything stays on record.
SIEM · EDR
Alert raised
A security alert fires in the monitoring system.
System
AI agent
Triage
Reads the alert and sorts it: real incident or noise.
AI agent
Context
Checked
Correlates assets, accounts and known vulnerabilities.
AI agent
Proposal
Response ready
Prepares the action: isolate, block, rotate the password.
AI agent
Security team
Human approval
The analyst approves; without a person, nothing is applied.
Human
EDR · Firewall
Action applied
Isolation, blocking and cleanup run automatically.
AI agent
Report
Incident closed
The report lands on the ticket; the lesson becomes a new rule.
AI agent · Human
Every closed incident becomes a new detection rule
- Alerts are triaged in minutes, not hours.
- A person approves anything that touches production systems.
- Every incident leaves a report, not just a chat thread.
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