Governed through the AI Agent Gateway
Every agent deploys through the Scrums.AI Gateway: centralised governance, access control, audit trails and performance monitoring, with agent activity, decisions and outcomes visible in SEOP dashboards.
AI Agent Development Services
Custom AI agents built, integrated and run as scoped workstreams on the Scrums.com platform: support, document, research and voice agents for the business, and QA, review and release agents inside your SDLC. Every agent is governed through the AI Agent Gateway, and Scrums.com owns the outcome, milestone by milestone.
Delivering since 2012 · Trusted by 400+ enterprises · 40-60% cost savings versus US and UK firms
Outcome-driven sprintAI Agent RolloutOne operational AI agent deployed into a live workflow with governance and guardrails: workflow selection, agent build and integration, enterprise controls, and a measured rollout with hours-saved reporting.View scope Outcome-driven sprintCustomer Support AI AgentAn AI support agent with knowledge retrieval, tool actions, escalation rules and conversation tracking, resolving a measured share of support conversations without a human.View scope PodAutomation Engineering PodAn ongoing pod building out AI agents and workflow automations across your SDLC and operations, agent by agent, behind enterprise governance, with ROI measured on every rollout.View scope
60%
Less manual QA effort
3x
Faster release cycles
40%
Developer productivity gain
2-4 wks
Pilot agent to measured results
< 21 days
Agent workstream live
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AI agent development services on the Scrums.com platform
§ agents / aiScrums.com builds custom AI agents as scoped workstreams: one operational agent into one live workflow, with governance, guardrails and a measured rollout, then the next. This is agentic AI development for production, not a prototype handoff. The engineers who build your agents integrate them with your tools, train your team on them and keep tuning them from real usage. When you need to build AI agents across many workflows, an automation pod runs the backlog agent by agent, with ROI measured on every rollout.
Expert implementation, then optimisation
Specialists implement agents alongside your team, configure workflows to your needs and train your engineers. Agents learn from your codebase and deployment history, and configurations are iterated from team feedback.
Human gates where they matter
Agent actions are monitored, logged and reviewable. Critical operations such as production deployments and code merges carry human approval gates, with rollback and gradual rollout from pilot teams outward.
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AI agents for business, and agents inside your SDLC
§ agents / typesCustomer support AI agents
AI support agents with knowledge retrieval, tool actions, escalation rules and conversation tracking. The finish state is a measured share of support conversations resolved well without a human.
- Knowledge retrieval over your approved content
- Tool actions inside your support stack
- Escalation rules that hand off to people
- Conversation tracking and resolution reporting
Document processing and compliance agents
Agents that extract structured fields and classifications from documents with validation and exception handling, and agents that monitor compliance across multi-step workflows. Documents become clean structured data automatically, with people only on the exceptions.
- Structured fields and classifications from documents
- Validation and exception handling
- Compliance monitoring across multi-step workflows
- People only on the exceptions
Sales and research agents
Agents that gather, structure and summarise account and prospect information from approved sources and tools, so reps open calls with a current, sourced account brief they did not write.
- Research from approved sources and tools only
- Structured, sourced account briefs
- Summaries ready before the call
Voice AI agents
A working voice agent for one bounded call flow, with speech, tools, state and handoff behaviour, completing real calls, with an honest read on production readiness before it scales.
- One bounded call flow, end to end
- Tool use and state across the conversation
- Handoff to a person when the flow requires it
QA, code review and release agents
Agents that generate test cases and run regression suites, review every pull request for quality, security and performance issues, and orchestrate releases across environments with blue-green rollouts and rollback. Manual QA effort drops by 60% and release cycles run 3x faster.
- Test generation, execution and deployment validation
- Automated pull request reviews and refactoring suggestions
- Multi-environment release orchestration with rollback
- Code standards held without manual oversight
DevOps and workflow orchestration agents
Agents that automate infrastructure provisioning, monitoring setup and incident response, and agents that run sprint planning, backlog grooming, dependency tracking and progress reporting in Jira, Azure DevOps and ClickUp, cutting project management overhead by 50%.
- Scaling on load patterns and self-healing for common issues
- Incident response with human approval gates
- Tickets updated and blockers flagged automatically
- Progress reporting without manual effort
Build or deploy
Every agent above is a fixed-scope workstream with agreed acceptance criteria and milestones. When a ready-made agent already fits, deploy it from the AI Catalog instead of building one.
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Enterprise AI agents: security, evaluation and control
§ agents / controlEnterprise AI agents need the same controls as any production system. Every agent runs behind the gateway with the security, evaluation and approval controls below, from the first pilot.
Security Data and access
- Data sovereigntyYour data never leaves your environment for model training.
- Access control and auditPermissions management and an audit trail for every agent action.
- ComplianceAlignment with SOC 2, GDPR and HIPAA standards, over encrypted communication channels.
- Regulated deploymentsOn-premise deployment, bring-your-own-model architectures and custom compliance configurations.
Control Quality and approval
- Evaluation and guardrailsTest sets, scoring, red-team checks, policy controls and release gates for LLM-powered agents, so quality and safety are measured and gated.
- Human approval gatesCritical operations such as production deployments and code merges wait for approval, set to your risk tolerance.
- Performance trackingAutomation coverage, task completion accuracy and time saved against manual processes, tracked in SEOP dashboards.
- Gradual rolloutPilot teams first, then expansion, so issues surface before they reach the whole organisation.
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AI agent development process: discovery to scale
§ agents / runPilots with one team and one workflow deploy in 2 to 4 weeks with measurable results inside the first month. Standard rollouts across teams take 2 to 3 months; enterprise programmes run 6 to 12 months in phases.
Process Four phases
- Discovery and architectureWorkflows, toolchain and pain points assessed; high-impact automation candidates mapped; agent architecture, integrations, governance framework and success metrics defined.
- Agent setupSEOP and AI Gateway environment set up, tools integrated (Jira, GitHub, Azure DevOps, Jenkins, monitoring), pilot agents deployed, access control configured and a pilot group onboarded.
- RolloutLow-risk automation first, such as testing and code analysis, then higher-impact areas such as deployment orchestration and incident response, as value is demonstrated.
- Scale and optimiseROI measured, agent accuracy improved through feedback loops, new use cases implemented, and quarterly performance reports with expansion recommendations.
Stack Where agents plug in
- Delivery toolsJira, Azure DevOps, GitHub, GitLab and Bitbucket; Jenkins, CircleCI and GitLab CI.
- Cloud and runtimeAWS (CodePipeline, CloudWatch), Azure (Pipelines, Monitor), Kubernetes, Docker and Terraform.
- Communication and on-callSlack, Microsoft Teams and PagerDuty, with Datadog and New Relic for monitoring.
- Your own systemsCustom internal tools through REST APIs, with agent workflows designed around your existing processes.
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When to build custom AI agents
§ agents / fitIt makes sense when
Manual QA bottlenecks slow your releases
Automated test generation, execution and regression coverage reduce QA time by 60%.
Release cycles are long and manual
Automated orchestration deploys 3x faster, with rollback built in.
Pull requests wait days for review
AI code review gives instant feedback on every commit.
Your DevOps team is overwhelmed
Agents automate 50% of repetitive operational work: infrastructure tasks, monitoring alerts and incident response.
Governance concerns block AI adoption
The AI Gateway provides enterprise-grade control, audit trails and data sovereignty.
Consider alternatives when
An agent that already exists does the job
Deployable agents from the AI Catalog install without a build. Browse AI agents →
You need governance for agents you already run
Access control, metering and audit across your agents is the gateway, not a build. AI Agent Gateway →
Your workflow is deterministic
Not every automation problem needs an LLM. Well-defined inputs are more predictable and cheaper to run as traditional integration work. Integration scopes →
You want AI engineers in a team you direct
Add vetted AI engineers from the register to your own team. Hire AI engineers →
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AI agent development FAQs
§ agents / faqWhat types of AI agents do you build?
Agents for the business and agents inside the software development lifecycle. Business agents cover customer support, document extraction and compliance monitoring, sales and account research, and voice call flows. SDLC agents cover QA (test generation, execution, validation), code review (quality analysis, security scanning, refactoring suggestions), DevOps (provisioning, monitoring, incident response), release orchestration, development assistance and SDLC workflow (sprint planning, backlog grooming, progress tracking). All of them run through the Scrums.AI Gateway with unified governance and visibility.
How long does AI agent development take?
A pilot with focused automation, one team and one workflow, typically deploys in 2 to 4 weeks with measurable results inside the first month. Standard implementations across several teams and workflows take 2 to 3 months from discovery to production rollout. Enterprise programmes run 6 to 12 months with phased deployment, change management and continuous optimisation. Integration complexity, team size and automation maturity set the timeline.
What does AI agent development cost?
Agent work runs on the Scrums.com delivery plans: the Sprint plan is $4,699 per month for one work stream, and larger plans run more work streams at once. As industry planning ranges, a single-team pilot is roughly $25K to $75K over 2 to 3 months, a multi-team rollout $75K to $200K over 6 months, and an organisation-wide programme $200K to $800K or more. Scope of automation, team size, integration complexity and governance needs drive the figure.
Will AI agents replace our developers and QA engineers?
No. Agents augment people rather than replace them. QA engineers move from manual test execution to test strategy, edge-case identification and exploratory testing. Developers focus on complex business logic and architecture while agents handle boilerplate, repetitive tasks and routine reviews. Most clients see productivity gains of 40-60% with the same headcount.
How do you keep AI agents secure?
Every agent deploys through the Scrums.AI Gateway with data sovereignty controls, so your data never leaves your environment for training; access control and permissions management; audit trails for all agent actions; model governance and explainability; SOC 2, GDPR and HIPAA alignment; and encrypted communication channels. Regulated industries can deploy on-premise, bring their own model, and use custom compliance configurations.
What happens if an AI agent makes a mistake?
All agent actions are monitored, logged and reviewable through SEOP dashboards. Critical operations such as production deployments and code merges have human approval gates, configured to your risk tolerance. Agents include rollback, the team monitors agent performance to correct errors quickly, and gradual rollout from pilot teams catches issues before organisation-wide impact.
Can AI agents integrate with our existing tools?
Yes. The gateway integrates with Jira, Azure DevOps, GitHub, GitLab, Bitbucket, Jenkins, CircleCI, GitLab CI, AWS, Azure, Kubernetes, Docker, Terraform, Slack, Microsoft Teams, PagerDuty, Datadog, New Relic and custom internal tools through REST APIs. Agent workflows are designed around your existing processes rather than forcing process change.
Do we need AI expertise in-house?
No. The workstream brings the AI expertise: agent configuration, workflow design, integration, training and ongoing optimisation. Your team learns agent capabilities and good practice, but you do not need data scientists or ML engineers to benefit from automation.
How do you measure AI agent performance?
SEOP dashboards track automation coverage, task completion accuracy, time saved against manual processes, bug catch rate for QA agents, deployment success rate for DevOps agents, code quality improvement for review agents and developer satisfaction. Baselines are set before implementation, and quarterly reviews assess ROI and plan the next agents.
Can we start with a pilot?
Yes, and it is the recommended start for organisations new to AI agents. A typical pilot focuses on one high-impact use case, such as QA automation for one team or code review for specific repositories, and runs 1 to 2 months. Successful pilots expand in steps based on measured results and team feedback.
AI AGENT DEVELOPMENT · SORTED
Agents in production, governed and measured.
Scoped AI agent workstreams on the Scrums.com platform: support, document, research, voice and SDLC agents behind the AI Agent Gateway, with Scrums.com accountable for the outcome.
SUPPORT · DOCUMENTS · RESEARCH · VOICE · SDLC