Getting Started
Welcome to TuringForce! This guide will help you set up your first AI agent and start automating your development workflow.
Quick Start
- Sign up for a TuringForce account
- Connect your development tools
- Configure your first agent
- Start automating your workflow
Prerequisites
- Active development environment
- Git repository access
- Project management tool (Jira, Azure DevOps, etc.)
Platform Guide
Learn about TuringForce's core platform capabilities and how to configure them for your organization.
Security & Privacy
Configure enterprise-grade security settings including encryption, access controls, and compliance features.
Agent Orchestration
Set up intelligent task coordination between multiple AI agents to optimize your development workflow.
Agents
Detailed documentation for each TuringForce agent and how to configure them for your specific needs.
Purpose
The Backlog Grooming Agent ensures that user stories in Jira are well-defined, business-valuable, and sprint-ready before they are accepted into planning. By applying a standardized Story Quality Scoring Model, this agent gives Product Owners, Scrum Masters, and engineering leaders a consistent way to measure and improve backlog health — reducing rework, clarifying intent, and increasing sprint predictability.
Why It Matters
Poorly written or ambiguous stories often lead to back-and-forth clarification, blocked developers, reduced sprint velocity, and missed business commitments. The Backlog Grooming Agent helps avoid these issues by:
- Improving predictability — only clear, testable, valuable stories enter the sprint.
- Increasing velocity & reducing waste — less time spent refining during development.
- Providing leadership visibility — report on story readiness and its impact on delivery risk.
- Strengthening accountability — teams can see measurable improvement in story quality.
Story Quality Scoring Model
Each story is automatically evaluated and scored in Jira across four key parameters (max total: 10 points):
Business Value (3 points)
Clear connection to business objectives, user needs, or strategic goals
Acceptance Criteria (3 points)
Well-defined, testable criteria that specify when the story is complete
Technical Clarity (2 points)
Sufficient technical detail for developers to estimate and implement
Dependencies (2 points)
Clear identification of blockers, prerequisites, or external dependencies
Workflow
The Backlog Grooming Agent operates continuously in the background, monitoring your Jira backlog for new or updated stories:
- Story Detection: Scans for new stories, updates, or status changes in configured Jira projects
- Quality Assessment: Applies the Story Quality Scoring Model to evaluate each story
- Automated Actions: Updates story fields, adds comments, or triggers notifications based on scores
- Reporting: Generates daily/weekly reports on backlog health and improvement trends
Reporting & Metrics
The agent provides comprehensive visibility into backlog health through:
- Quality Score Distribution: Histogram showing score distribution across your backlog
- Improvement Trends: Track quality improvements over time by team, project, or epic
- Velocity Impact: Correlation between story quality and sprint velocity
- Blocked Stories: Identification of stories stuck in low-quality states
Outcome
Teams using the Backlog Grooming Agent typically see:
- 25-40% reduction in story clarification time during sprints
- 15-30% improvement in sprint velocity and predictability
- 60% faster time-to-ready for sprint planning
- Clear visibility into backlog health for leadership and stakeholders
Clear, data-driven view of backlog health to forecast delivery risk and take corrective action early.
Purpose
The Sprint Planning Agent helps product owners and engineering leaders plan sprints with precision and predictability. It analyzes backlog health, team velocity, dependencies, and risks to recommend the right scope for each sprint — ensuring commitments are realistic and delivery is reliable.
Why It Matters
Sprint planning often suffers from:
- Over-commitment — teams take on more work than their velocity supports.
- Under-commitment — teams leave capacity unused due to conservative estimates.
- Hidden dependencies — work gets blocked mid-sprint due to unnoticed external dependencies.
- Unbalanced load — some team members are overloaded while others have idle time.
The Sprint Planning Agent removes guesswork by analyzing historical data and real-time signals to recommend optimal sprint scope.
Core Capabilities
📊
Velocity-Based Forecasting
- Calculates team velocity from last 3 sprints (configurable).
- Adjusts for planned time off, holidays, or team changes.
- Recommends story point capacity for upcoming sprint.
🔗
Dependency Detection
- Scans Jira links (blocks, is blocked by) to identify cross-story dependencies.
- Flags stories with unresolved blockers or external team dependencies.
- Warns if dependent work isn't scheduled in the same sprint.
⚖️
Load Balancing
- Distributes work evenly across team members based on capacity and skills.
- Highlights over-allocated or under-utilized team members.
- Suggests reassignments to optimize throughput.
🎯
Sprint Goal Alignment
- Ensures selected stories align with the defined sprint goal.
- Flags low-priority or off-goal work that could be deferred.
- Recommends high-value stories from the backlog to fill capacity.
Workflow
Agent analyzes backlog health, team velocity, and capacity.
Identifies dependencies and risks in candidate stories.
Generates a recommended sprint scope based on data.
Team reviews agent recommendations in Jira or Confluence.
Agent provides real-time feedback as stories are added/removed.
Flags over-commitment or under-utilization dynamically.
Agent publishes sprint summary to Confluence.
Sends sprint commitments and key risks to Slack.
Tracks sprint health metrics throughout execution.
Reporting & Metrics
📈
Recommended vs. Actual Capacity
Comparison of agent-recommended story points vs. team's final commitment.
🔗
Dependency Risk Score
% of stories with unresolved blockers or cross-team dependencies.
⚖️
Load Balance Index
Distribution of work across team members (even = healthy).
🎯
Sprint Goal Alignment
% of committed stories aligned with sprint goal.
Outcome
🎯
Realistic Commitments
Data-driven capacity planning reduces over-commitment and improves sprint success rates.
🔗
Fewer Mid-Sprint Surprises
Dependency detection prevents unexpected blockers during execution.
⚖️
Balanced Team Workload
Load balancing prevents burnout and maximizes throughput.
📊
Improved Predictability
Consistent velocity tracking and forecasting build stakeholder confidence.
☀️
Daily Standup Agent
Automated Sprint Status & Insights
Purpose
The Daily Standup Agent automates sprint status tracking and provides intelligent insights to help teams identify blockers, optimize workflow, and maintain momentum. By analyzing Jira updates, Git commits, and team activity, this agent delivers actionable standup summaries and proactive recommendations.
Why It Matters
Effective standups are crucial for sprint success, but they often become routine status updates without actionable insights. The Daily Standup Agent enhances standups by:
- Automating status collection — reduces manual reporting and focuses on blockers
- Providing data-driven insights — identifies patterns and bottlenecks automatically
- Enabling proactive intervention — flags risks before they impact sprint goals
- Improving team coordination — highlights dependencies and collaboration opportunities
Standup Quality Scoring Model
The agent evaluates standup effectiveness and sprint health across key dimensions:
Progress Visibility (3 points)
Clear tracking of story progress and completion status
Blocker Identification (3 points)
Early detection and escalation of impediments
Team Coordination (2 points)
Effective collaboration and dependency management
Sprint Alignment (2 points)
Progress toward sprint goals and commitments
Workflow
The Daily Standup Agent operates continuously throughout the sprint:
- Status Aggregation: Collects updates from Jira, Git, and other tools
- Insight Generation: Analyzes patterns and identifies trends
- Standup Preparation: Generates agenda and highlights key issues
- Follow-up Tracking: Monitors resolution of identified blockers
Reporting & Metrics
Comprehensive standup analytics and sprint insights:
- Sprint Velocity Tracking: Real-time progress toward sprint goals
- Blocker Analysis: Common impediments and resolution patterns
- Team Performance: Individual and collective productivity metrics
- Predictive Insights: Early warning indicators for sprint risks
Outcome
Teams using the Daily Standup Agent typically achieve:
- 50-70% reduction in manual status reporting time
- 30-50% faster blocker resolution through early detection
- 25-40% improvement in sprint completion rates
- Enhanced team focus on value delivery rather than status updates
Automated standup insights that transform routine status meetings into strategic sprint optimization sessions.
Purpose
The Risk & Dependency Agent proactively identifies and monitors potential risks, dependencies, and blockers that could impact sprint delivery. By analyzing story relationships, team capacity, external dependencies, and historical patterns, this agent provides early warning signals and actionable recommendations to prevent sprint failures.
Why It Matters
Unidentified risks and dependencies are the leading cause of sprint failures and missed commitments. The Risk & Dependency Agent helps prevent these issues by:
- Early risk detection — identifies potential blockers before they impact delivery
- Dependency mapping — visualizes and tracks complex story relationships
- Predictive analytics — forecasts sprint risks based on historical patterns
- Proactive mitigation — suggests actions to prevent or minimize impact
Risk Quality Scoring Model
The agent evaluates sprint risks across multiple dimensions to provide comprehensive risk assessment:
Dependency Risk (3 points)
External dependencies and their potential impact on delivery
Capacity Risk (3 points)
Team capacity vs. sprint commitments and complexity
Technical Risk (2 points)
Technical complexity and potential implementation challenges
Timeline Risk (2 points)
Schedule constraints and delivery timeline pressures
Workflow
The Risk & Dependency Agent operates continuously throughout the sprint lifecycle:
- Risk Assessment: Analyzes stories, dependencies, and team capacity
- Dependency Mapping: Identifies and visualizes story relationships
- Risk Monitoring: Tracks risk evolution and new threats
- Mitigation Recommendations: Suggests actions to reduce or eliminate risks
Reporting & Metrics
Comprehensive risk analytics and sprint health insights:
- Risk Dashboard: Real-time view of sprint risks and their impact
- Dependency Graph: Visual representation of story relationships
- Risk Trends: Historical risk patterns and improvement tracking
- Mitigation Tracking: Progress on risk reduction actions
Outcome
Teams using the Risk & Dependency Agent typically achieve:
- 60-80% reduction in unexpected sprint blockers
- 40-60% improvement in sprint completion rates
- 50-70% faster risk identification and mitigation
- Enhanced predictability through proactive risk management
Proactive risk management that prevents sprint failures and ensures reliable delivery.
Purpose
The Status Reporting Agent automates the generation of comprehensive sprint and project status reports, providing stakeholders with timely, accurate, and actionable insights. By analyzing team activity, story progress, and key metrics, this agent delivers professional status updates that keep everyone informed and aligned.
Why It Matters
Effective status reporting is crucial for project success, but manual reporting is time-consuming and often inconsistent. The Status Reporting Agent enhances reporting by:
- Automating report generation — reduces manual effort and ensures consistency
- Providing real-time insights — delivers up-to-date project status and metrics
- Enabling proactive communication — identifies and escalates issues early
- Improving stakeholder engagement — clear, professional reports keep everyone informed
Status Quality Scoring Model
The agent evaluates status report quality across multiple dimensions to ensure comprehensive and actionable reporting:
Progress Clarity (3 points)
Clear, quantifiable progress indicators and completion status
Risk Visibility (3 points)
Identification and communication of potential issues and blockers
Stakeholder Value (2 points)
Relevant, actionable information for different stakeholder groups
Timeliness (2 points)
Up-to-date information and prompt delivery of reports
Workflow
The Status Reporting Agent operates on a scheduled basis and responds to events:
- Data Collection: Aggregates information from Jira, Git, and other tools
- Analysis & Insights: Processes data to identify trends and issues
- Report Generation: Creates formatted reports for different audiences
- Distribution: Delivers reports via email, Slack, or other channels
Reporting & Metrics
Comprehensive status reporting analytics and insights:
- Report Quality Metrics: Tracking of report completeness and stakeholder satisfaction
- Communication Effectiveness: Analysis of stakeholder engagement and response
- Issue Resolution Tracking: Monitoring of identified issues and their resolution
- Stakeholder Feedback: Collection and analysis of report recipient feedback
Outcome
Teams using the Status Reporting Agent typically achieve:
- 70-90% reduction in manual reporting effort
- 50-70% improvement in stakeholder satisfaction with project visibility
- 40-60% faster issue identification and escalation
- Enhanced transparency and trust through consistent, professional reporting
Automated status reporting that keeps stakeholders informed and projects on track.
Purpose
The Cost & Resource Optimization Agent continuously monitors and optimizes AI agent performance, infrastructure usage, and resource allocation to maximize efficiency and minimize costs. By analyzing usage patterns, performance metrics, and cost data, this agent provides intelligent recommendations for resource optimization and cost reduction.
Why It Matters
Inefficient resource usage and uncontrolled costs can quickly impact project profitability and scalability. The Cost & Resource Optimization Agent helps prevent these issues by:
- Optimizing resource allocation — ensures efficient use of compute, storage, and network resources
- Reducing operational costs — identifies and eliminates unnecessary spending
- Improving performance efficiency — maximizes output per unit of resource consumed
- Enabling scalable growth — provides cost-effective scaling strategies
Cost Optimization Scoring Model
The agent evaluates cost efficiency and resource optimization across multiple dimensions:
Resource Utilization (3 points)
Efficient use of compute, storage, and network resources
Cost Efficiency (3 points)
Optimal cost per unit of value delivered
Performance Optimization (2 points)
Maximizing output while minimizing resource consumption
Scalability Planning (2 points)
Cost-effective scaling strategies and capacity planning
Workflow
The Cost & Resource Optimization Agent operates continuously to monitor and optimize costs:
- Cost Monitoring: Tracks spending across all resources and services
- Usage Analysis: Analyzes resource utilization patterns and inefficiencies
- Optimization Recommendations: Suggests specific cost reduction strategies
- Automated Actions: Implements approved optimizations automatically
Reporting & Metrics
Comprehensive cost optimization analytics and insights:
- Cost Dashboard: Real-time view of spending and cost trends
- Resource Utilization Reports: Analysis of resource efficiency and waste
- Optimization Impact: Tracking of cost savings from implemented optimizations
- Forecasting: Predictive cost analysis and budget planning
Outcome
Teams using the Cost & Resource Optimization Agent typically achieve:
- 30-50% reduction in operational costs
- 40-60% improvement in resource utilization efficiency
- 25-40% faster cost optimization through automated recommendations
- Enhanced scalability through cost-effective growth strategies
Intelligent cost optimization that maximizes value while minimizing expenses.
Purpose
The Developer Experience (DevEx) Module empowers customers to create, customize, and deploy their own AI agents safely — without waiting for vendor engineering. A visual builder plus integrated code editor allows teams to design workflows, connect data, add intelligence, and push new PMO AI agents to production with security, cost control, and compliance built in.
Why It Matters
Enterprises want to adapt AI automation to their exact delivery workflows, but traditional customization is slow and requires engineering support. A secure, self-service DevEx solves this by:
⚡
Enabling rapid innovation — build and test new AI agents without deep coding expertise. Reducing time-to-value — no waiting for vendor professional services or custom code releases. Maintaining control — fine-grained RBAC, cost guardrails, and compliance checks built into the platform. Scaling safely — let teams experiment while protecting production systems and budgets.
Core Capabilities
🎨
Visual Builder (No-Code)
- Drag-and-drop interface for workflows (Jira queries, Confluence sync, Slack updates).
- Prebuilt building blocks: data sources, transforms (filter, summarize, classify), actions (notify, create ticket, publish page).
- Real-time validation and test mode with sample data.
⌨️
Integrated Code Editor (Pro-Code)
- Embedded Monaco/VS Code experience with syntax highlighting and auto-completion.
- Secure SDK access for retrieval (BM25 + vectors), memory APIs, and connectors.
- Linting, policy checks, and sandboxed execution to keep agents safe.
📋
Versioning & Governance
- Git-backed or platform-native version control for rollback and audit.
- Approval workflows and MFA for publishing new agents.
- RBAC to restrict who can build, edit, and deploy.
💰
Cost & Resource Awareness
- Pre-deployment cost estimate (token usage, infra resources).
- Real-time spend and utilization dashboards for each agent.
- Alerts when usage or cost deviates from set thresholds.
🔒
Secure Context Windows & Data Contracts
- Enforces what data each agent can access, prevents schema drift, and protects sensitive information.
Workflow
Start with templates or visual blocks, add custom logic if needed.
Run in sandbox with synthetic or scoped tenant data.
Automated security checks, cost guardrails, and compliance policy scan.
Push approved version to workspace; updates propagate instantly.
Usage, cost, and performance dashboards help refine agents over time.
Reporting & Metrics
⚡
Agent Build Velocity
Number of new agents and time from concept to deploy.
💰
Cost per Agent
Spend tracking by agent (compute, LLM calls, vector DB).
✅
Governance Compliance
% of agents passing policy and security checks.
🔄
Version Stability
Rollback frequency and deployment success rates.
Outcome
⚡
Faster Innovation
Non-technical and technical users create agents in hours, not weeks.
💰
Cost-Safe Scaling
Built-in FinOps guardrails keep AI operations predictable and efficient.
🔒
Secure & Compliant
RBAC, context boundaries, and audit logging protect sensitive data.
📈
Continuous Improvement
Teams can iterate on agents safely and learn from real-time metrics.
A Smarter Brain for Your PMO AI Agent
Our Memory & Knowledge layer transforms the PMO AI Agent from a one-off assistant into a trusted partner that remembers your projects, understands change, and keeps leadership aligned.
With hybrid retrieval, workspace memory, and secure data contracts, every update, report, and insight gets sharper over time — without sacrificing control or compliance.
Key Capabilities
🔎
Precision + Understanding — Hybrid Retrieval
- Combines BM25 keyword search and semantic vector search to find the most relevant Jira issues, Confluence pages, and decisions.
- Captures both exact terms ("Velocity trend Q2") and context ("stories slipping across sprints").
🧠
Workspace Memory
- Persistent knowledge that grows with every sprint: decisions, blockers, and outcomes.
- Enables long-term context so agents spot patterns, recommend proactively, and get smarter with each cycle.
📄
Data Contracts
- Enforce consistent definitions for metrics like Story Quality Score, velocity, risk.
- Stops schema drift so reporting is accurate across teams and time.
🔒
Secure Context Windows
- Granular access control and context limits: the agent only sees what it should.
- Integrates with SSO, SCIM, RBAC to protect sensitive information while enabling rich insights.
How It Works
Pulls from Jira, Confluence, Slack, and other approved systems.
Maintains project history and semantic relationships in workspace memory.
Fetches just the right information at query time, governed by your security model.
Feeds contextual intelligence to features like Backlog Grooming, Standups, Status Reporting, and Compliance.
Benefits
🎯
Always Relevant
AI answers are grounded in your actual work and history.
📊
Consistent & Auditable
Standardized metrics and data contracts support leadership and compliance.
🔒
Secure by Design
Data stays within defined boundaries; sensitive content is protected.
📈
Smarter Over Time
Each sprint builds knowledge that improves recommendations and reporting.
API Reference
Complete API documentation for integrating TuringForce with your existing tools and workflows.
Authentication
Learn how to authenticate with the TuringForce API using API keys and OAuth.
Endpoints
Comprehensive list of all available API endpoints with examples and response formats.
Integrations
Connect TuringForce with your favorite development tools and platforms.
Supported Tools
- Jira
- Azure DevOps
- GitHub
- GitLab
- Jenkins
- Slack
- Microsoft Teams
Troubleshooting
Common issues and solutions to help you resolve problems quickly.
Common Issues
- Agent connection problems
- Integration failures
- Performance issues
- Authentication errors