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 AI Platform Engineering project are exposed via the A2A protocol, standardizing external I/O and providing authentication and authorization support.

System Diagram​

Sequence Diagram​

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

2. 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

3. Rewrite history to move everything into ai_platform_engineering/agents/$AGENT_NAME:

git filter-repo --to-subdirectory-filter ai_platform_engineering/agents/$AGENT_NAME

4. 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

5. Create a branch

git checkout -b migrate_agent_$AGENT_NAME

6. Merge the imported history (use --allow-unrelated-histories the first time):

git merge $AGENT_NAME-temp/main --allow-unrelated-histories

7. 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

8. Upload branch and PR

git push origin migrate_agent_$AGENT_NAME