Skip to main content

Agentic Board

An MCP-driven ticket platform where coding agents claim, analyse, implement and test work, with linked evidence and a human quality gate.

At a glance

Project type
Prototype
Year
2026
Project stage
Active
Role of 5A Technologies
Co-development with Infium, agentic architecture, MCP workflow design, quality gates and product implementation.
Technologies
  • TypeScript
  • Next.js
  • React
  • Node.js
  • PostgreSQL
  • Redis
Agentic Board — controlled agentic delivery flowHover or focus for details. On mobile, tap a phase to open it.

Ten-phase Agentic Board workflow turning plans into MCP tickets, board context and priorities before agent analysis, implementation, QA, pull-request sync and human review. 1. Structure the plan: Functional, technical, security and QA input 2. Build tickets through MCP: Phases receive scope, criteria and dependencies 3. Connect board context: Sprint, team, repository and agent pool 4. Weigh and prioritise: Value, risk, permissions and dependencies 5. Make ticket executable: Scope and criteria are ready for claiming 6. Agent claims ticket: A suitable permitted task is locked 7. Analyse context and code: Approach, risks and blockers are identified 8. Implement and validate: Code, tests and required checks 9. Synchronise PR and board: Evidence, status and PR remain linked 10. Human quality gate: Approve, rework, reject or block Connections: Structure the plan to Build tickets through MCP (structured plan). Build tickets through MCP to Connect board context (tickets with criteria). Connect board context to Weigh and prioritise (board and project context). Weigh and prioritise to Make ticket executable (weighted executability). Make ticket executable to Agent claims ticket (permitted ticket). Agent claims ticket to Analyse context and code (locked assignment). Analyse context and code to Implement and validate (bounded approach). Implement and validate to Synchronise PR and board (code, tests and evidence). Synchronise PR and board to Human quality gate (review-ready pull request). Human quality gate to Analyse context and code (changes requested). Synchronise PR and board to Make ticket executable (next permitted ticket).

Agentic Board · system flow10 phases · human quality gate and rework
  1. Structure the planFunctional, technical, security and QA input
    Plan, executable ticket and claimA plan is divided into executable parts

    Functional, technical, security and QA input remains the source for scope, acceptance criteria and quality requirements for every phase and subphase.

  2. Build tickets through MCPPhases receive scope, criteria and dependencies
    MCP, board context, prioritisation and board syncThe ticket factory creates a controlled execution contract

    Every executable part receives scope, acceptance criteria, dependencies, sprint, repository, risk, evidence requirements, allowed actions and review policy.

  3. Connect board contextSprint, team, repository and agent pool
    MCP, board context, prioritisation and board syncProject context and access are connected to the ticket

    Board, Wiki and Test Plan provide relevant project rules, repository context, test requirements, permissions and available runner capabilities.

  4. Weigh and prioritiseValue, risk, permissions and dependencies
    MCP, board context, prioritisation and board syncNot every ticket is executable at every moment

    Priority, weight, dependencies, repository, risk, permissions and runner capabilities determine which work can be taken on responsibly.

  5. Make ticket executableScope and criteria are ready for claiming
    Plan, executable ticket and claimThe ticket connects planning, execution and review

    An executable ticket forms the fixed agreement between the original request, agent actions, required quality evidence and human assessment.

  6. Agent claims ticketA suitable permitted task is locked
    Agent analysis, implementation and QAClaiming prevents simultaneous execution of the same work

    A suitable runner claims one permitted ticket. Locking and idempotency bound duplicate execution and keep ownership and status visible on the board.

  7. Analyse context and codeApproach, risks and blockers are identified
    Agent analysis, implementation and QAThe agent examines the assignment before implementation

    The runner analyses permitted repository context, dependencies, acceptance criteria, risks and missing information and records a bounded approach.

  8. Implement and validateCode, tests and required checks
    Agent analysis, implementation and QAChanges and quality evidence are created in a bounded workspace

    The agent makes the change and runs required build, test, security and accessibility checks. An unresolved blocker remains explicitly visible for human input.

  9. Synchronise PR and boardEvidence, status and PR remain linked
    MCP, board context, prioritisation and board syncThe ticket receives the controlled execution result

    Commits, checks, evidence, remaining risks and the pull-request reference are linked to the same ticket and the original acceptance criteria.

  10. Human quality gateApprove, rework, reject or block
    Human quality gate and reworkA reviewer decides what happens to the change

    A person can approve, request targeted changes, reject or block. Merging, deployment, release and closure happen only when configured policy explicitly permits each action.

Plan, executable ticket and claimMCP, board context, prioritisation and board syncAgent analysis, implementation and QAHuman quality gate and reworkAgentic Board links planning, ticket context, agent execution, evidence and review; rework and ticket selection use separate feedback loops.

After board sync, a runner can take another permitted ticket while review remains active separately.

Merging, deployment, release and closure remain subject to explicitly configured policy.

Claiming, idempotency and locking prevent runners from executing the same ticket simultaneously.

A blocker requires human context, access or an external decision.

In brief

The problem

The solution in four phases

Platform architecture

Hosted and dedicated models

Our contribution and qualitative result

Deliberate boundaries

What this project demonstrates

Would you like to discuss a similar solution?

Tell us where your process slows down today or where AI and automation should work together more effectively. We will explore which controlled approach fits.