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AI Agent vs MCP Server

· 3 min read
Sri Aradhyula
Sri Aradhyula

Introduction​

Agentic Systems landscape is evolving rapidly, understanding the distinction between AI Agents and MCP Servers is crucial for building scalable agentic systems. While MCP Servers provide a standardized interface for tools and data sources, AI Agents leverage these capabilities to perform complex reasoning, planning, and execution tasks.

MCP​

MCP (Model Context Protocol) provides a standardized interface for LLMs to access tools and data sources.

Agent​

  • Agents are systems that use LLM-based reasoning to plan and take actions—including invoking MCP tools when needed.
  • Agents can handle complex tasks that could require multiple MCP tools and are capable of maintaining both short-term and long-term memory.
  • Agents encapsulate more than simple tool calls to MCP servers, providing an additional abstraction layer.

Agent Composition & Capabilities​

  • Tool Pruning: Optimizes the toolset for efficiency and relevance, including filtering tools from large MCP servers (with optional RAG for selection).
  • Long-term and short-term memory management: Agents utilize short-term memory for session-specific context and long-term memory for cross-session data recall.
  • Agent Registry: Manages agent versions and configurations.
  • Prompty Library: Provides a versioned repository for managing and evaluating prompts.
  • MCP Registry: Handles MCP server versions and configurations.
  • Maintain Conversation Context: Preserves conversation history within a thread for more effective LLM reasoning and action.
  • Prompt Engineering: Shapes agent behavior with well-designed system prompts.
  • Evaluation: Validates agent actions using standardized rubrics and tool trajectory audits.
  • Flexible LLM Bindings: Supports various LLM providers (e.g., GPT, Claude, Mistral).

This architecture makes agents composable, validated black-box units that can be reused across multi-agent systems and different personas.

Note: Agents built in CAIPE (Community AI Platform Engineering) project are exposed via the A2A protocol, standardizing external I/O and providing authentication and authorization support.

System Diagram​

Sequence Diagram​

migrate-to-monorepo

· One min read

Instructions to migrate agent repos to mono repo

export AGENT_NAME=foo
  1. Clone the Target Monorepo (ai-platform-engineering)
cd /tmp
git clone git@github.com:cnoe-io/ai-platform-engineering.git
  1. Clone agent-$AGENT_NAME to a temp location:
cd /tmp
git clone git@github.com:cnoe-io/agent-$AGENT_NAME.git agent-$AGENT_NAME-temp
cd agent-$AGENT_NAME-temp
  1. Rewrite history to move everything into ai_platform_engineering/agents/$AGENT_NAME:
git filter-repo --to-subdirectory-filter ai_platform_engineering/agents/$AGENT_NAME
  1. Now add this rewritten repo as a remote to your monorepo:
cd ../ai-platform-engineering    # Go back to your main repo
git remote add $AGENT_NAME-temp ../agent-$AGENT_NAME-temp
git fetch $AGENT_NAME-temp
  1. Create a branch
git checkout -b migrate_agent_$AGENT_NAME
  1. Merge the imported history (use --allow-unrelated-histories the first time):
git merge $AGENT_NAME-temp/main --allow-unrelated-histories
  1. Clean Up (Optional)
git remote remove $AGENT_NAME-temp
read -p "Delete ../agent-$AGENT_NAME-temp and all contents? [y/N] " ans && [[ $ans =~ ^[Yy]$ ]] && rm -rf ../agent-$AGENT_NAME-temp
  1. Upload branch and PR
git push origin migrate_agent_$AGENT_NAME