Agentic AI Workflow

 

MCP vs Skills

Both MCP and Skills extend what an agent can do. But they solve different problems, and picking the wrong one adds cost or complexity you don't need.

The diagram breaks down the five dimensions that matter.

1. Integration: MCP is a client-server protocol that connects N agents to M backends through one interface. Agent Skills are folders with a SKILL. md that the agent loads on trigger.
2. Architecture: MCP runs as a separate process with its own runtime, speaking JSON-RPC. A Skill is just a directory: SKILL. md, optional scripts, references, and assets.
3. Invocation: MCP tools are called with typed parameters validated against a schema, and can be chained. Skills are invoked by the agent reading SKILL. md and running whatever commands it describes like bash, python, or curl.
4. Runtime: MCP servers often run in their own container or service. Skills run in the agent's own environment with no extra infra.
5. Where it fits: Use MCP to connect agents to live systems and data. Use Skills to give agents reusable know-how and instructions.

10 Pillars of AI Agent

Real agents aren’t just LLMs. They’re full systems - with memory, reasoning, tools, workflows, and guardrails working together.

Here are the 10 Pillars of Agentic AI broken down in simple terms:

1️⃣ Goal Understanding & Intent Parsing
An agent must accurately interpret what the user wants - the goal, constraints, and context - before doing anything.

2️⃣ Memory Systems (Short-Term + Long-Term)
Agents need a way to store, retrieve, and update relevant information over time, both episodic and semantic memory.

3️⃣ Reasoning & Planning Engine
The agent thinks through steps, plans actions, and corrects itself when needed using chain-of-thought reasoning and self-reflection loops.

4️⃣ Tool Use & API Integration
Agents must act on the world, not just generate text. This means calling APIs, executing functions, and orchestrating tools.

5️⃣ Workflow Orchestration
Real systems need sequences, branching logic, triggers, retries, and multi-step coordination - not just one-off responses.

6️⃣ Knowledge Integration (Private + External Data)
Agents pull structured and unstructured data from internal sources, RAG pipelines, databases, and the web to stay grounded.

7️⃣ Learning & Adaptation
Feedback, corrections, and repeated interactions make agents smarter over time - updating preferences, prompts, and behavior.

8️⃣ Security, Safety & Guardrails
Agents must follow rules: permissions, constraints, data protection, and ethical boundaries to prevent harmful or unsafe actions.

9️⃣ Multi-Agent Collaboration
Multiple agents can coordinate, hand off tasks, or specialize (planner, executor, critic), improving accuracy and speed.

🔟 Execution & Real-World Action Interface
Agents must actually do things: run scripts, generate files, update systems, schedule tasks, or trigger workflows.

Agentic AI isn’t “just a smarter chatbot.”
It’s a full-stack architecture - reasoning + memory + tools + workflows + guardrails - working together to deliver real outcomes.