How a Tax Tech Firm Reduced Agile Overhead with Agent Scrum on AWS

Executive Summary 

A leading automated tax compliance software provider sought to streamline and automate its Agile ceremonies across globally distributed engineering teams. Manual backlog refinement, sprint planning, and daily standups were time-consuming and inconsistent across squads. By deploying AgentScrum on AWS, they implemented a suite of intelligent agents that integrated directly with Jira, MS Teams, Git, and Outlook.

These agents facilitated real-time backlog prioritisation, sprint planning, and impediment tracking while enforcing compliance and auditability. Within 8 weeks, they reduced Scrum Master workload by 60%, achieved a 3x improvement in sprint throughput visibility, and cut planning overhead by over 40%. The deployment followed AWS Well-Architected best practices and ensured scalability across 40+ engineering squads. 

Customer Challenge 

Business Challenges 

Engineering teams relied on manual, ad hoc practices for Agile ceremonies. Each Scrum Master used their techniques for sprint planning, backlog grooming, and impediment tracking. This led to inconsistent sprint quality, delayed planning cycles, and difficulty identifying systemic issues across teams. Product Owners struggled to enforce prioritisation models like WSJF due to the overhead of Jira management. Meanwhile, executives lacked real-time visibility into sprint health and impediments. 

Goals included: 

• Reducing time spent on non-development activities (ceremonies, reporting) 

• Improving team alignment and transparency across sprints 

• Enforcing consistent backlog and sprint hygiene 

• Meeting internal audit and compliance standards for engineering processes 

Their existing approach, based on manual facilitation via Jira, Confluence, and MS Teams, could not scale. It lacked audit trails, real-time alerts, and proactive coaching. Critical business pressures included rolling out a quarterly roadmap under regulatory deadlines and unifying agile reporting across squads. 

Technical Challenges 

Legacy processes relied on disparate Jira projects, inconsistent sprint rituals, and siloed Slack/MS Teams communication. Key technical issues included: 

• Inability to normalise sprint metrics across 40+ Jira boards 

• Technical debt in backlog grooming due to unclear priorities and dependencies 

• Missing linkage between Git commits, Jira status, and team availability 

• No single source of truth for impediments, reviews, and retrospective outcomes 

• Security requirements for data encryption at rest and in transit (SOC 2)

• Integration across Outlook calendars, MS Teams threads, and Jira automation

Partner Solution 

Solution Overview 

AgentScrum was deployed as a managed agent framework on AWS, purpose-built for their Agile workflow. Nine LLM-powered agents were introduced to cover the full sprint lifecycle: from backlog triage and WSJF-based prioritisation to sprint planning, daily Scrum orchestration, and retrospective facilitation. All interactions occurred inside their MS Teams channels and Jira. Context was pulled from Jira, Git, Outlook, and Teams using a Multi-Connector Platform (MCP) hosted in VPC. Agent decisions were human-in-loop and audit-tracked. 

The architecture included a LangGraph orchestration layer, mem0 vector storage for context memory, and dashboards hosted on CloudFront. All components adhered to AWS security and performance guidelines. 

AWS Services Used 

• Amazon EKS: To run containerised agents at scale with auto-scaling policies 

• Amazon S3: To store agent logs, standup transcripts, and retrospective reports 

• Amazon RDS (PostgreSQL): For structured backlog metadata and sprint state 

• Amazon ElastiCache (Redis): For short-lived session context during planning and standups 

• Amazon Bedrock: Used for prompt management, multi-agent coordination, and output evaluation 

• Amazon Cognito: For secure user access control and RBAC 

• AWS Secrets Manager: To store OAuth keys for Jira, Teams, GitHub 

• Amazon CloudWatch: For centralised log aggregation and alerting 

• Amazon SNS: For alerting team leads about blockers, delays, or approval needs 

Architecture Diagram 

Implementation Details 

Our customer adopted a phased implementation using Agile best practices. The project began with pilot squads in their tax compliance division. Within two weeks, agents for backlog refinement and sprint planning were deployed. A sprint planning agent proposed backlog decomposition and prioritisation using WSJF scores and capacity-aware assignment. Standup agents were added in week 3, pulling task updates from Jira and prompting team members inside MS Teams. 

In phase two, agents for impediment tracking, sprint review, and retrospectives were added. These agents leveraged mem0 for storing historical impediment data and facilitated async retrospectives by surfacing patterns across sprints. 

Security was enforced via VPC deployment, IAM roles, and data flow encryption. The team followed DevSecOps with CI/CD pipelines for agent updates. All Jira/Git/MS Teams interactions were sandboxed and required explicit writeback approvals. 

The full rollout spanned six weeks. Major milestones included: 

• Week 1–2: AgentPilot setup, Slack/Teams/Jira integration 

• Week 3: Planning agent and Standup agent rollout 

• Week 4–5: Review, Impediment, Retrospective agents 

• Week 6: Reporting dashboards + full context memory bootstrapped 

Innovation and Best Practices 

AgentScrum leveraged AWS Well-Architected Framework pillars: Security (KMS + IAM), Reliability (auto-scaling EKS agents), Operational Excellence (CloudWatch metrics), and Cost Optimisation (on-demand Bedrock access). The use of Amazon Bedrock for prompt versioning and agent behaviour evaluation was a key differentiator. 

The team also used an innovative multi-agent coordination pattern using LangGraph and Redis pub-sub channels to support long-running ceremonies. All agents ran stateless containers for resilience. DevOps pipelines pushed YAML-based agent updates with canary rollouts. 

Results and Benefits 

Business Outcomes and Success Metrics 

• 60% reduction in Scrum Master time spent on facilitation and reporting 

• 3x increase in sprint visibility for leadership through automated dashboards 

• 42% decrease in planning cycle time due to WSJF auto-prioritisation 

• 27% faster impediment resolution through agent-managed escalation 

• Full compliance with SOC 2 audit requirements for agile delivery reporting 

• ROI achieved in under 90 days from deployment 

Technical Benefits 

• Burndown charts are auto-updated every 30 minutes via agent ingestion 

• Cycle time variance reduced by 35% due to improved backlog hygiene 

• Elastic agent scaling allowed support for 400+ developers across time zones 

• Mem0 context memory enabled cross-sprint intelligence (e.g., repeat blockers) 

• RBAC enforcement with Cognito prevented data leakage between teams 

• Git + Jira alignment led to 22% fewer orphaned tasks 

Lessons Learned 

Challenges Overcome 

• Initial resistance from Scrum Masters used to manual facilitation was overcome by offering Slack-based transparency logs and reversible agent suggestions. 

• Git integration had inconsistencies due to varied repo structures, resolved by mapping context anchors to project prefixes. 

• Prompt fatigue during retrospectives was fixed with context-aware summarisation agents that shortened ceremony length by 25%. 

Best Practices Identified 

• Always start a pilot with high-coordination teams (e.g., tax compliance squads) 

• Use DevOps to deploy agents via canary rollouts with rollback capability 

• Incorporate user override buttons for every agent suggestion 

• Align WSJF fields directly inside Jira for agent traceability 

• Monitor sentiment via MS Teams metadata (reaction signals, read time) 

Future Plans 

They plan to expand AgentScrum to its QA and DevOps teams to automate regression review and deployment readiness checks. Upcoming integrations include ServiceNow and Figma. 

They also plan to implement cost-tracking agents using internal FinOps data via Athena queries. The ongoing partnership will include prompt tuning on Bedrock Titan and the introduction of sentiment-based team health dashboards via Amazon QuickSight.