Delivery Plan · Core
Now $4,500£3,750R79,000/ monthWas $8,000£6,700R140,000
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AI Agent Development on a Monthly Delivery Plan
Get custom AI agents that take repeat work off your teams, governed for your business workflows and your SDLC. Through the Software Factory Delivery Plan, you get one team, one predictable monthly price and one prioritised backlog. Add mobile, web, backend, data, QA or DevOps work to the same backlog whenever you need it.
Delivering since 201248 hours to first codeManaged live on Scrums.com
Use your plan for AI agent development.
14DAYSFirst deliverable in 14 days or your first month is free.
ThousandsOf companies since 2012
Trusted by engineering leaders at ambitious startups, enterprises and global brands.
Read client stories →
How your Software Factory Delivery Plan works
How the AI agent development process works
- 01Queue your agent workPick agent scopes or describe your own.
- 02SubscribeCheck out and onboarding starts immediately.
- 03First code in production in 48 hoursAccess to your repos, tools and environments.
- 04Pilot, measure, scaleTrack every agent rollout on the Scrums.com Platform.
Scrums.com Platform
Track every agent build and rollout on the Scrums.com Platform
Goals break down into tasks. Milestones are agreed before work begins. You track every agent from discovery to a measured rollout, and the next task starts the moment one ships.
Requirement9
Tool actions for refunds and order lookup
AI5 pts
Test set and scoring for the support agent
QA5 pts
Helpdesk connector through the REST API
Backend3 pts
Human approval gate above the refund limit
DevOps3 pts
Delivering1
Knowledge retrieval over approved contentAI
- Agent workflow mapped
- Evaluation set passes
- Code review approved
- Agent live behind the gateway
AI eng · agentsPR #1820
Gate
Live38
Conversation tracking and resolution report
AI · just nowAudit trail for every agent action
DevOps · 4d agoBaseline for time saved against manual work
Data · 6d agoAgent architecture and success metrics
AI · 8d agoIllustrative demo with sample data. On the Scrums.com Platform, the board shows your own goals, tasks and releases.
Scope and capacity
What one workstream delivers, for AI agents or any software
Your backlog can hold any number of requests, but the team delivers them one workstream at a time. A workstream is one managed team working on your highest-priority request until it is live, then starting the next. More requests mean more months, not faster delivery. Work is scoped and delivered within your plan’s capacity. Priorities can change; additional parallel workstreams require more capacity. The same plan covers mobile, web, backend, data, QA and DevOps work, so all of it can share one backlog.
- Active at once
- One request
- Pilot agent
- 2 to 4 weeks
- Rollout across teams
- 2 to 3 months
- Code owned by
- You
Example: a pilot agent on one workstream
Illustrative- Month 1Discovery and architecture: workflows and toolchain assessed, the first agent, its integrations and success metrics defined. Then the pilot agent deployed for one team and one workflow, behind the AI Agent Gateway.
- Month 2Rollout: low-risk automation first, such as testing and code analysis, then higher-impact areas such as deployment orchestration and incident response, as value is demonstrated.
- Month 3Scale and optimise: ROI measured, agent accuracy improved through feedback loops, and the next use cases added to the backlog.
A sample agent programme. It matches the 2 to 4 weeks a pilot with one team and one workflow typically takes, and the 2 to 3 months a standard rollout across teams takes. Onboarding starts immediately after checkout, and your delivery owner scopes your own plan with you.
What affects the number of months
- Scope of automationThe number of workflows and agents, and how many teams use them.
- Integration complexityEach tool an agent acts in, such as Jira, GitHub, your helpdesk or your own APIs, adds integration work.
- Team sizeMore teams mean a longer rollout, onboarding and change management.
- Automation maturityTeams with existing automation and clean processes move faster than teams starting from manual work.
- Governance needsApproval gates, audit trails, on-premise deployment and compliance alignment add configuration work.
- Review and approvalsHow quickly your team agrees acceptance criteria, approval thresholds and sign-off.
Capabilities
AI agents for business: support, document, research, voice and SDLC agents.
Customer support AI agents
Knowledge retrieval · tool actions · escalationAI 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.
When it fitsYour support team answers the same questions again and again, and you want a measured share resolved without a person.
Trade-offsThe agent resolves a measured share of conversations, not all of them. Escalation rules hand the rest to people.
How the plan covers it
Queue this scope →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
Extraction · validation · exceptionsAgents 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.
When it fitsYour team keys data from documents by hand, or checks compliance step by step across a workflow.
Trade-offsPeople stay on the exceptions. A deterministic workflow with well-defined inputs can be cheaper and more predictable as traditional integration work.
How the plan covers it
Queue this scope →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
Approved sources · structured briefsAgents 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.
When it fitsReps prepare account briefs by hand before each call.
Trade-offsThe agent uses approved sources and tools only. The scope agrees those sources before the build starts.
How the plan covers it
Queue this scope →Research from approved sources and tools only
Structured, sourced account briefs
Summaries ready before the call
Voice AI agents
Speech · tools · state · handoffA 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.
When it fitsYou have one bounded call flow that you want an agent to complete end to end.
Trade-offsThe prototype covers one call flow. You get an honest read on production readiness before the agent scales.
How the plan covers it
Queue this scope →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
Test generation · PR review · CI/CDAgents 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.
When it fitsManual QA bottlenecks slow your releases, release cycles are long and manual, or pull requests wait days for review.
Trade-offsCritical operations such as code merges and production deployments wait for human approval, set to your risk tolerance.
How the plan covers it
Queue this scope →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
Provisioning · incidents · Jira · ClickUpAgents 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%.
When it fitsYour DevOps team is overwhelmed by repetitive infrastructure tasks, monitoring alerts and incident response.
Trade-offsIncident response runs with human approval gates. Rollout starts with pilot teams, so issues surface before they reach the whole organisation.
How the plan covers it
Queue this scope →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
What's included in AI agent development
Discovery, build, governance and rollout. Managed together.
Every Delivery Plan · Core comes with the full agent team and the controls around it: discovery, agent build and integration, evaluation, governance through the AI Agent Gateway and a measured rollout, run by one delivery owner.
Delivery managementOne accountable delivery owner.Milestones agreed before work begins.
01Discovery and architectureWorkflows, toolchain and pain points assessed; agent architecture, integrations and success metrics defined
02Agent build and integrationAgents connected to Jira, GitHub, Azure DevOps, Jenkins, Slack and your own tools through REST APIs
03AI Agent Gateway governanceAccess control, audit trails and performance monitoring for every agent
04Evaluation and guardrailsTest sets, scoring, red-team checks, policy controls and release gates
05Human approval gatesCritical operations such as production deployments and code merges wait for approval
06Data sovereignty and complianceYour data never leaves your environment for model training; SOC 2, GDPR and HIPAA alignment
07Performance trackingAutomation coverage, task completion accuracy and time saved against manual processes
08Team trainingYour team learns agent capabilities and good practice; no in-house data scientists needed
10Code ownershipAll agent code, configurations and integrations are yours from day one.
100%A live agent is not the finish line.
Feedback loopsAgent accuracy improved from real usage and team feedback.
Gradual rolloutPilot teams first, then expansion, so issues surface early.
New use casesThe next agents planned from measured results.
Quarterly reportsPerformance reports with ROI and expansion recommendations.
Runs on the Scrums.com platform
Every Delivery Plan runs on the Enterprise AI Platform for software engineering.
Your team, its AI agents and your delivery data sit on one governed platform. That is what powers your delivery board, the AI Agent Gateway that governs your agents, and the talent bench behind your plan.
Your planDelivery Plan · CoreAI agent, QA, web, mobile, backend and data scopes on one backlogSee pricing →TalentAI engineers and teamsCertified engineers, CTOs and dedicated teamsHire engineers →AI Catalog163 agents · 101 MCPs · 30 modelsDeployable AI for engineering orgsBrowse the catalog →PlatformEnterprise AI PlatformAgent harness, AI and MCP gateways, governanceExplore the platform →
What your agent team draws on, already part of your planBrowse the full AI catalog →
Engineering talent
AI engineers and teams
AI engineers and agent specialists from the Scrums.com bench, matched to your workflows, tools and domain.
AI Catalog
Agents, MCPs and models for your agents
Deployable agents that install without a build, plus the MCP servers and models your custom agents use. When a ready agent already fits, deploy it instead of building one.
AI infrastructure
Infrastructure for agents in production
Runtime, model and data infrastructure for your agents, deployable from the catalog with your $500 first-month credits.
Compare
One AI hire covers one role. The plan covers the whole team.
Production agents need discovery, build, integration, evaluation and governance work. Hiring covers them one role at a time. The Delivery Plan covers them in one monthly price.
Compare
RecommendedScrums.com Delivery Plan · Core
In-house AI engineer
Traditional AI agency
Roles covered
Discovery, agent build, integration, evaluation, governance
One role per hire
Depends on the agency bench
Time to start
48 hours to first code in production
A hiring cycle first
Proposal and contract cycle first
How you pay
Salary, benefits and recruiting fees
Project quotes and change requests
Who runs delivery
One accountable delivery owner
You manage the work
The agency project manager
Visibility
Every agent task, approval and accuracy metric on the Scrums.com Platform
Your own tooling
Status reports by email
40-60%†cost savings versus US and UK firms
Start for Basis: the figure that the current Scrums.com AI agent development page states for delivery cost against US and UK firms. It is not a quote for your scope.
What you get
Why companies choose a managed team for enterprise AI agents
01
Governed from the first pilot
Every agent deploys through the AI Agent Gateway, with access control, audit trails and performance monitoring.
02
One monthly price
One plan price for discovery, build and rollout. Your scope sets the number of months.
03
Human gates where they matter
Critical operations wait for human approval, with rollback and gradual rollout from pilot teams outward.
04
Your data stays yours
Your data never leaves your environment for model training. Regulated teams can deploy on-premise or bring their own model.
05
Measured, not guessed
Baselines are set before implementation. Time saved, accuracy and coverage are tracked, with quarterly ROI reviews.
06
No in-house AI team needed
The team brings the agent configuration, workflow design, integration and training. Your people keep the judgment work.
Customers
AI agent development case studies
Agent teams are matched to your domain, built for banking, financial services and other regulated industries, on the AI Agent Gateway and the tools you already run.
Featured AI agent case study
AI agents in banking: 11,058 hours saved in 30 days
- Challenge
- A leading financial services group had to migrate legacy APIs and rebuild data models across more than a million lines of code in 5,000+ files. Its architects estimated months of work for 40 or more senior engineers.
- Platforms and technologies
- The AI Agent Gateway inside the client's own AWS account via Amazon Bedrock, a shared memory layer native to MCP, and Jira, Confluence, Bitbucket and GitHub.
- Work delivered
- A governed fleet of discovery, scoping, build, review, test, architecture and security agents. 858 sub-systems documented, 987,151 lines of legacy code mapped and 30 pull requests reviewed.
- Measurable result
- 11,058 engineering hours saved in 30 days: a 75x velocity gain on about 149 hours of machine time, with a 97.5% success rate over 32,815 tool calls.
Scopes
AI agent scopes you can queue
One Delivery Plan · Core covers your development priorities across services. Start with AI agents, then queue mobile, web, backend, data or QA scopes on the same backlog, or describe your own.
AGTAI agents5 scopesGENGenerative AIExplore ↗APIBackend & APIsExplore ↗DEData engineeringExplore ↗QAQA & testingExplore ↗OPSDevOps & reliabilityExplore ↗WWeb appsExplore ↗MMobile appsExplore ↗→All scopesCatalog
SprintSC-01
AI Agent Rollout
One 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.
PodSC-02
Automation Engineering Pod
An ongoing pod building out AI agents and workflow automations across your SDLC and operations: an automation backlog delivered agent by agent, behind enterprise governance, with ROI measured on every rollout.
SprintSC-03
Customer Support AI Agent
Implement an AI support agent with knowledge retrieval, tool actions, escalation rules, and conversation tracking. Finish state: a share of support conversations resolved well without a human, measured.
SprintSC-04
Sales Research AI Agent
Build an agent that gathers, structures, and summarizes account and prospect information from approved sources and tools. Finish state: reps opening calls with a current, sourced account brief they did not write.
SprintSC-05
Voice AI Agent Prototype
Create a working voice agent for one bounded call flow with speech, tools, state, and handoff behavior. Finish state: a demonstrable voice agent completing real calls on one flow, with an honest read on production readiness.
The plan in full
AI agent development services pricing
Core costs Was $8,000£6,700R140,000 now $4,500£3,750R79,000/month for one active workstream. Your agents’ scope determines the delivery schedule and total cost. You can use the same subscription for other development priorities.
Best for companies where manual QA, long release cycles, slow reviews or an overwhelmed DevOps team hold back delivery, or where governance concerns block AI adoption.
Delivery Plan · Core
One plan for your software backlog.
Now $4,500£3,750R79,000/ monthWas $8,000£6,700R140,000
One active workstream. Managed delivery.
Start your Delivery Plan →
Billed monthly · excl. VAT/sales tax · onboarding starts immediately after checkout
Included every month
Delivery owner. One accountable owner runs your AI engineers, integration engineers and QA.
One backlog across your development priorities. You set the order; requests are scoped and delivered one workstream at a time.
Visibility. Milestones agreed up front. Agent tasks, approvals and performance live on the Scrums.com Platform.
Ownership. Agent code, configurations and integrations are yours from day one, with handover included.
Work is scoped and delivered within your plan’s capacity. Priorities can change; additional parallel workstreams require more capacity.
First-month benefits
14DAYS
First-delivery guaranteeYour first agreed deliverable ships within 14 days of scope and access being confirmed, or your first month is free.
$500 in AI infrastructure catalog creditsMONTH 1
Annual-plan benefits$10,000 in specialist services
Technical Assessment & Delivery RoadmapWe review your systems, rank your priorities and agree the first deliverable.
CI/CD & Quality Pipeline SetupYour pipelines, environments and quality checks, set up and handed over to you.
You buy monthly delivery capacity, not a fixed project price. Infrastructure usage and third-party services are billed separately. Full Delivery Plan coverage and terms →
Need more parallel workstreams? Talk to us →
Day 0Scope and access confirmedOnboarding starts at checkout. You confirm scope and share repos, tools and environments.
Within 48 hoursFirst code in productionFirst change merged and deployed through your pipeline.
Within 14 daysFirst agreed deliverable shipsFor example the first agent workflow mapped, with its architecture, integrations and success metrics agreed.
If it does not shipYour first month is freeYou do not pay for month one.
The first deliverable is agreed in writing before the clock starts. The clock starts when scope and access are confirmed. Applies to the first deliverable on a new plan.Read the full Delivery Plan terms →
FAQ
AI agent development questions, answered
Still deciding? Talk to sales and get a scoped answer for your workflows.
Delivery Plan · Core costs Was $8,000£6,700R140,000 now $4,500£3,750R79,000 a month, excluding VAT or sales tax. That buys one active workstream with a managed AI agent team. It is monthly delivery capacity, not a fixed price for a complete agent programme, so your scope sets the number of months.
Yes. Delivery Plan · Core covers mobile, web, backend, data, QA and DevOps work as well as AI agent development. All of it sits in one prioritised backlog and is delivered within your plan’s capacity, one active workstream at a time. Priorities can change; additional parallel workstreams require more capacity.
Discovery and agent architecture, agent build and integration with your tools, governance through the AI Agent Gateway, evaluation and guardrails, human approval gates, performance tracking, team training, and one accountable delivery owner. Every plan includes $500 in AI infrastructure catalog credits in your first month. Annual plans also include $10,000 in specialist services: a technical assessment, a delivery roadmap and CI/CD pipeline setup.
You rank the backlog. Your delivery owner scopes the top request, agrees acceptance criteria with you, and the team delivers it. You can reorder anything that has not started. One request is active at a time on Core; additional parallel workstreams require more capacity.
The clock starts when you confirm scope and give access to your repositories, tools and environments. Within 48 hours the team has its first code in production in your environment. Within 14 days your first agreed deliverable ships, for example the first agent workflow mapped, with its architecture, integrations and success metrics agreed. If it does not, your first month is free.
48 hours to first code in production means the first change merged and deployed through your pipeline, counted from the day scope and access are confirmed. 14 days means the deliverable named at kickoff ships within 14 days of that date, or your first month is free. “Thousands of companies” counts organisations that have bought engineering services from Scrums.com since 2012. 2 to 4 weeks from pilot agent to measured results, 60% less manual QA effort, 3x faster release cycles and 50% less project management overhead are the figures the current Scrums.com AI agent development page states. 40-60% cost savings is the figure that page states for delivery cost against US and UK firms; it is not a quote for your scope.
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, code review, DevOps, release orchestration and SDLC workflow such as sprint planning and progress tracking. All of them run through the AI Agent Gateway with one set of governance and visibility.
A pilot with 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 in phases. Integration complexity, team size and automation maturity set the timeline.
No. Agents augment people. QA engineers move from manual test execution to test strategy, edge cases and exploratory testing. Developers focus on complex business logic and architecture while agents take boilerplate, repetitive tasks and routine reviews.
Every agent deploys through the AI Agent Gateway. Your data never leaves your environment for training. You get access control, permissions management, audit trails for all agent actions, 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.
All agent actions are monitored, logged and reviewable on the Scrums.com Platform. Critical operations such as production deployments and code merges have human approval gates, set to your risk tolerance. Agents include rollback, and a gradual rollout from pilot teams catches issues before they reach the whole organisation.
Yes. Agents integrate with Jira, Azure DevOps, GitHub, GitLab, Bitbucket, Jenkins, CircleCI, AWS, Azure, Kubernetes, Docker, Terraform, Slack, Microsoft Teams, PagerDuty, Datadog, New Relic and your own internal tools through REST APIs. Agent workflows are designed around your existing processes.
No. The team 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.
The Scrums.com Platform tracks automation coverage, task completion accuracy, time saved against manual processes, bug catch rate for QA agents, deployment success rate for DevOps agents and code quality improvement for review agents. Baselines are set before implementation, and quarterly reviews assess ROI and plan the next agents.
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.
Delivery Plan · Core: one monthly price, one backlog across your development priorities, one active workstream and a managed team for support, document, research, voice and SDLC agents behind the AI Agent Gateway.
SUPPORT · DOCUMENTS · RESEARCH · VOICE · SDLC