Agent Lifecycle & Workflow Design
Overview
Every MCP-powered Agent in MindCore follows a clear lifecycle—from initial design to real-time deployment and evolution. Understanding this lifecycle helps you build, monitor, and improve agents with purpose and precision.
🛠️ 1. Compose (Design Your Agent)
Start by defining the agent’s purpose:
What task will it perform?
What context does it need?
What MCP modules will it depend on?
Using either the No-Code Builder or raw config files, compose the agent using:
MCP Modules (data sources, analytics, web scrapers)
LLM (for logic, interpretation, summarization)
Triggers & Conditions (e.g., time-based, threshold-based)
Actions (e.g., send alert, call webhook, store output)
🧩 2. Harmonize (Test & Calibrate)
Before going live:
Run test executions
Validate inputs/outputs
Simulate trigger scenarios
Review AI reasoning and output structure
You can also A/B test different MCP module combinations and logic flows.
🚀 3. Perform (Deploy to Network)
When ready:
Deploy the agent on the decentralized MindCore runtime
Choose execution type:
Manual: User-triggered
Scheduled: Recurring (e.g., every 10 min)
Event-Driven: React to live data or MCP outputs
Monitor agent performance via logs, output history, and module call stats.
🔁 4. Iterate (Refine & Upgrade)
Post-deployment, agents can be:
Updated: Swap or reconfigure modules, logic, or LLMs
Versioned: Publish upgrades while preserving old versions
Cloned: Used as templates for new agents
Track performance, usage, and earnings over time to improve logic and utility.
Best Practices
Keep agents narrow and focused: 1 task = 1 agent
Reuse tested MCP modules
Monitor logic regularly based on market or data shifts
Version early, version often
What’s Next?
Now that you understand how agents live and evolve, let’s explore how governance and future upgrades will shape the protocol.
Last updated