Jira A2A Proof of Concept

Overview

The Jira A2A system is a DevOps workflow automation platform using the tRPC-A2A-Go framework. It consists of independent Go agents that communicate via A2A messages, each implementing the standard TaskProcessor interface.

Agent-to-Agent (A2A) Communication

Agent-to-Agent (A2A) is a decentralized messaging pattern where independent services (agents) exchange strongly-typed tasks and results over a transport layer—in this case, gRPC—without tight coupling or shared state. Each agent publishes and subscribes to tasks using the trpc-a2a-go library, which:

  • Defines a common TaskProcessor interface for sending and handling tasks.

  • Serializes tasks as protobuf messages and transports them over gRPC.

  • Handles routing, retry, and backoff transparently between agents.

For more details, see the official trpc-a2a-go GitHub repo and its pkg.go.dev documentation.

System Architecture

System Sequence Diagram

The following Mermaid sequence diagram illustrates the step-by-step message flow between major system components:

Key Components

  • JiraRetrievalAgent: Handles Jira webhook events, extracts ticket information, coordinates with other agents, and posts analysis results back to Jira.

  • InformationGatheringAgent: Analyzes ticket information, integrates with LLM for intelligent analysis, generates insights and recommendations, and returns structured analysis results.

  • CopilotAgent (Planned): Receives implementation requirements, integrates with GitHub Copilot API, generates code, and creates pull requests.

  • LLM Integration: Provides AI-powered analysis, generates summaries and insights, identifies missing information, and recommends next actions.

  • tRPC-A2A-Go Library: Underlying agent-to-agent communication framework used by this system; see the upstream repo for details.

System Workflow

  1. Webhook Reception: JiraRetrievalAgent receives a webhook from Jira

  2. Data Extraction: JiraRetrievalAgent extracts ticket details

  3. Task Creation: JiraRetrievalAgent creates a "ticket-available" task

  4. Analysis Request: JiraRetrievalAgent sends the task to InformationGatheringAgent

  5. Information Analysis: InformationGatheringAgent processes the task with LLM assistance

  6. Results Return: InformationGatheringAgent returns structured insights to JiraRetrievalAgent

  7. Jira Update: JiraRetrievalAgent posts a comment to the Jira ticket

  8. Code Implementation: (Future) JiraRetrievalAgent triggers CopilotAgent for code generation

  9. Pull Request Creation: (Future) CopilotAgent creates a GitHub pull request

Current Implementation Status

  • JiraRetrievalAgent: Fully implemented (webhook handling, Jira API integration, task coordination)

  • InformationGatheringAgent: Fully implemented (task processing, LLM integration, analysis generation)

  • CopilotAgent: Planned, not implemented

Component Details

JiraRetrievalAgent

Purpose: Handles Jira webhooks, coordinates agents, and updates Jira tickets.

Key Functions:

  • Processes incoming Jira webhook events

  • Retrieves ticket information from Jira API

  • Transforms Jira data into structured tasks

  • Sends tasks to the InformationGatheringAgent

  • Posts analysis results back to Jira tickets

Webhook Example:

{
  "timestamp": 1714824000000,
  "webhookEvent": "jira:issue_created",
  "issue_event_type_name": "issue_created",
  "user": {
    "accountId": "5d1234567890abcdef123456",
    "displayName": "Alice Example"
  },
  "issue": {
    "id": "10002",
    "key": "PROJ-456",
    "fields": {
      "summary": "Cannot log in to the system",
      "description": "User reports being unable to log in with correct credentials.",
      "issuetype": { "name": "Bug" },
      "priority": { "name": "High" }
    }
  }
}

InformationGatheringAgent

Purpose: Analyzes ticket information to provide structured insights and recommendations.

Key Functions:

  • Receives "ticket-available" tasks from JiraRetrievalAgent

  • Processes ticket data using LLM for analysis

  • Generates structured insights and recommendations

  • Returns analysis results to JiraRetrievalAgent

Analysis Example:

{
  "ticketId": "PROJ-123",
  "analysisResult": {
    "keyThemes": "authentication, login, security",
    "riskLevel": "high",
    "priority": "high",
    "technicalImpact": "Users unable to access the system, potential security issue",
    "businessImpact": "Loss of productivity, customer frustration",
    "suggestedComponents": "auth-service, user-management",
    "missingInformation": "Browser details, error messages, steps to reproduce"
  },
  "summary": "This appears to be a critical login issue affecting users' ability to access the system. It's likely related to the authentication service and should be addressed with high priority."
}

LLM Integration

  • Integrates with OpenAI API (or alternative LLM providers)

  • Implements prompt engineering for consistent analysis

  • Handles API communication, rate limiting, and error handling

  • Extracts structured data from LLM responses

Configuration Example:

LLM_ENABLED=true
LLM_PROVIDER=openai
LLM_MODEL=gpt-4
LLM_API_KEY=your-openai-api-key
LLM_MAX_TOKENS=4000
LLM_TEMPERATURE=0.0

Running the Application

Using Make

make run-all       # Start all agents
make run-info      # Start InformationGatheringAgent
make run-jira      # Start JiraRetrievalAgent
make logs-all      # View all logs
make stop-all      # Stop all agents

Using Docker Compose

docker-compose up -d

Manually

# Start InformationGatheringAgent
cd cmd/infogathering
go build
./infogathering

# Start JiraRetrievalAgent
cd cmd/jiraretrieval
go build
./jiraretrieval

Testing

Simulating a Jira Webhook

curl -X POST http://localhost:8081/webhook \
  -H "Content-Type: application/json" \
  -d '{
    "ticketId": "TEST-123",
    "event": "created"
  }'

Setup

Prerequisites

  • Go 1.16 or higher

  • Access to a Jira instance

Configuration

The application uses environment variables for configuration:

# Server configuration
export SERVER_PORT=8080
export SERVER_HOST=localhost

# Agent configuration
export AGENT_NAME=InformationGatheringAgent
export AGENT_VERSION=1.0.0
export AGENT_URL=http://localhost:8080

# Jira configuration
export JIRA_BASE_URL=https://your-jira-instance.atlassian.net
export JIRA_USERNAME=your-jira-username
export JIRA_API_TOKEN=your-jira-api-token

# Authentication
export AUTH_TYPE=apikey  # "jwt" or "apikey"
export API_KEY=your-api-key
export JWT_SECRET=your-jwt-secret  # Only needed if AUTH_TYPE=jwt

Running the Application

  1. Build and run the InformationGatheringAgent:

cd cmd/infogathering
go build
./infogathering

  1. The agent will start on the configured port (default: 8080) and listen for "ticket-available" tasks.

Testing

Simulating a Jira Webhook

You can simulate a Jira webhook by sending a POST request to the agent:

curl -X POST http://localhost:8080/task \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your-api-key" \
  -d '{
  "ticketId": "PROJ-123",
  "summary": "Implement new feature",
  "metadata": {
    "priority": "High",
    "reporter": "John Doe"
  }
}'

Using the Client Example

The client_example.go file contains examples of how to use the A2A client to send a "ticket-available" task and how to simulate a Jira webhook.

Future Work

  1. JiraRetrievalAgent

    • Listen for new Jira ticket webhooks

    • Emit a "ticket-available" A2A task with ticket information

Known Limitations and Future Improvements

  • Components are tightly coupled; refactoring for modularity is planned

  • Limited context in error messages; structured logging should be improved

  • Insufficient unit and integration tests; coverage will be expanded

  • Configuration validation and security enhancements are planned

  • CopilotAgent and code generation are planned for future releases

Contributing

  1. Fork the repository

  2. Create a feature branch

  3. Make your changes

  4. Write tests for your changes

  5. Submit a pull request

License

MIT

Nextdocs

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