Our Client
Payworks is a Canadian human capital management (HCM) and payroll technology provider. Its integrated platform spans payroll, HR, time, absence, recruiting, and workforce management on one shared data foundation, backed by expert, Canadian-based client support.
What problem did Payworks set out to solve?
Payworks made a deliberate decision: accelerate software delivery without loosening the human approvals, quality controls, and governance a regulated payroll business depends on. Its leaders had diagnosed the root causes clearly. Delivery practices had grown inconsistent across teams:
- Tooling and context sat in disconnected silos
- There was no standard approach to automated testing and coverage was thin
- Planning, development, QA, and release each carried their own friction
- Developers rediscovered the same context repeatedly
- Feedback loops ran slow
- AI adoption varied from team to team
Payworks had already invested in AI-assisted development. Those early efforts were instructive: they showed exactly what the foundation still lacked. Feedback loops, automated validation, and a shared context every role could trust were the missing pieces. Payworks drew the right conclusion from that experience. Scaling AI adoption on top of an unready foundation would multiply rework, widen regression exposure, and leave teams further from the self-sufficiency the investment was meant to build.
Payworks defined the brief precisely: a governed, repeatable agentic workflow running on live production work, built as reusable skills and playbooks, with stronger QA and test-management practices, and a hand-off model its own teams could run independently. Payworks chose SoftServe as the delivery partner to build it.
How did Payworks build a governed agentic workflow?
Payworks embedded a cross-functional SoftServe team directly into its delivery organization, with a clear mandate: build a governed agentic workflow on live production work, not a prototype. The engagement ran from April to August 2026, opening with onsite discovery in Winnipeg, Manitoba. Payworks’ own teams set the direction throughout. The joint team ran pilots on modernization and new feature development projects, then layered in automation, governance, and coaching on live tickets. Payworks later extended the model to a second squad, folding the product and business-analysis hand-off into the workflow and consolidating separate role skills into one cross-role operating model.
Payworks’ own business-analysis, development, QA, and UX leads drove execution in partnership with SoftServe: reviewing, approving, and adopting the workflow on production work. That was the design. The model had to take root inside Payworks’ teams, not inside a vendor. SoftServe contributed agentic engineering, AI, DevOps, QA automation, and program management expertise to support that goal.
The core change was structural. Payworks moved live work through one shared context, cross-role workflow instead of a set of separated, role-specific tools. A shared context anchor held requirements, code, and decisions against a single source of truth, so no role had to rediscover what another already knew. Explicit human approval gates also kept people in control of what advanced and what merged.
The workflow ran on four systems:
- Anthropic’s Claude and Claude Code powered role-specific skills across the lifecycle, helping analysts shape requirements and specs, developers scope and implement bounded changes, and QA engineers generate and structure tests.
- Enterprise code repository and CI/CD build and release pipelines.
- MCP integrations connected Claude to ticketing and technical documentation, where the shared Context Hub anchored every role against a single source of truth, so the workflow read and wrote against the same records the teams already used.
- Work decomposition, status, and traceability were anchored in the enterprise work management tool.
What results did Payworks achieve?
Within two months, one squad tripled its output. Story-point velocity and merged pull-request volume both rose roughly threefold against the squad's average across the sprints preceding the engagement, and its escaped-defect rate stayed unchanged.
We set out to prove that you can accelerate delivery with AI without loosening governance in a regulated payroll environment. Two months in, one squad had tripled its output. The answer was yes.VP Software Delivery Payworks
The second kind of evidence arrived in August 2026, during an onsite working session. A live sprint ticket moved completely end-to-end through the agentic workflow. It started as business-analysis requirements, passed into the shared Context Hub, went through a technical analysis pass, broke down into work management subtasks, turned into code changes across three separate repositories, went through review, and landed as a merged pull request.
One moment, in particular, settled the team’s confidence. During the technical pass, the workflow caught a missing dependency, an official French translation the change required, before implementation began. For a payroll provider serving employers across Canada, including Quebec, French-language parity is a release condition rather than a nice-to-have. Catching it in analysis versus after release showed the governed workflow held up under real conditions.
| BEFORE | AFTER |
|---|---|
| Each role rediscovered context in separate tools. |
One shared Context Hub anchored every role against a single source of truth. |
| No proven path from idea to production for agentic work. |
A live ticket reached a merged pull request across three repositories through the governed workflow. |
| Requirements gaps and dependencies surfaced late, after implementation. |
A technical pass caught a required dependency before implementation began. |
| No defined control points for AI-generated work. |
Explicit human approval gates governed what advanced and what merged. |
| No internal playbook for running agentic delivery unaided. |
A repeatable model the client’s own teams run. |
Payworks now owns a demonstrated, governed path for agentic delivery on production work: shared context, explicit role hand-offs, human approval gates, and reusable skills and playbooks its teams operate independently. That is the foundation Payworks built this engagement to create. It lowers the risk of disconnected, tool-by-tool AI adoption and gives Payworks room to scale delivery while protecting the quality and accountability its business depends on. The squads that took part run the workflow themselves today, with the skills and playbooks to extend it across the organization.
Which industries face the same challenge?
The same problems show up wherever teams have to ship fast against strict controls on legacy, multi-repository systems:
- Financial services: A platform engineering lead modernizing a core transactions system where every change has to keep its regulatory audit trail intact
- Insurance: A delivery manager pushing claims and policy updates across a tangle of aging repositories without weakening compliance sign-off
- Healthcare technology: An engineering director shipping features under strict privacy and validation rules, with a human required on every release decision
- Public-sector software: A product owner shipping citizen-facing services where an accessibility or language failure blocks release rather than trailing it
How could a governed agentic SDLC work for your teams?
Through partnership with SoftServe, Payworks built this capability on a live payroll platform in a regulated environment, with its own teams running it from day one. If your organization is still executing manually across requirements, coding, testing, and release, or is adopting AI tool by tool without shared context or governance, a governed agentic SDLC can help you move faster while keeping people in control.
Contact SoftServe’s agentic delivery team to explore where it fits within your workflows.
