Agents, Skills, or MCP? How to Orchestrate your AI Stack Without Creating New Bottlenecks
The discussion is not 'which one to use', but how to integrate the three levels of abstraction ensuring security, governance, and scale in engineering operations.
Fabiano Brito
CEO & Founder
Orchestrating your AI stack with Skills, MCP, and Agents is the practice of layering procedural instructions, secure data integration, and autonomous orchestration. For enterprises, this layered architecture is critical to scaling workflows without facing the runaway costs of pure agentic systems or the chaos of loose prompts.
With every new announcement in the AI ecosystem, engineering and product leaders face the same architectural question: “to automate our workflows, should we use Agents, Skills, or the Model Context Protocol (MCP)?”. The mature answer is not picking one — it’s adopting the three in layers, each with a distinct responsibility.
From loose-prompt chaos to layered architecture
Until recently the market lived at the extremes: either teams used autonomous agents for everything (blowing through budgets), or they ignored abstractions and relied on pasting massive instructions. Today the pieces fit together modularly — each layer answers a different question.
📘 Skills
Reusable procedural instructions (checklists, code-review patterns) that teach the AI to work your way.
- Lives in
- Team repository
- Marginal cost
- ~zero
- Risk
- Drift without review
🔌 MCP
Open protocol that gives the model access to external data without exposing credentials in the prompt — authentication is encapsulated by the server.
- Lives in
- Dedicated MCP server
- Marginal cost
- Low
- Risk
- Over-broad token scope
🧠 Agents
The orchestrator that thinks in logic loops, decides which Skill to apply, which MCP server to call, and when to delegate to another agent.
- Lives in
- Vertex AI / ADK
- Marginal cost
- High
- Risk
- Loops + runaway cost
The three golden rules in production
Skills, MCP and Agents only behave like production-grade infrastructure when these three rules are treated as pipeline blockers, not nice-to-haves:
MCP connections must always use scoped tokens. A server that only reads tickets has no business writing; one that queries the CRM should not see payroll.
No credential may live inside a SKILL.md. Skills describe procedure; authentication stays encapsulated in the matching MCP server.
When agents write code, the CI/CD pipeline is the mitigator against algorithmic hallucination. No green tests, no merge — period.
Risks and friction in orchestration
Without traceability of what one agent passes to another, the software supply chain becomes vulnerable. Log every MCP call, every Skill loaded, every handoff between agents — AI auditing is no different from financial auditing.
Scaling securely: the A-MAD approach
The A-MAD (AI-Managed Agile Development) methodology mitigates operational bottlenecks. In our pipeline on Google Cloud, flows use the Agent framework integrated with Vertex AI: Skills encode the customer’s particularities, tool integrations follow MCP, and QA and Development agents talk under tight governance. Separate layers, clear responsibility, predictable cost.
Frequently Asked Questions sobre Agents, Skills, or MCP? How to Orchestrate your AI Stack Without Creating New Bottlenecks
What is the best approach to automate workflows: Agents, Skills, or MCP? The most effective approach involves adopting all three (Agents, Skills, and MCP) in distinct layers, each with its specific responsibilities.
What are Skills in the context of AI architecture? Skills are reusable procedural instructions that teach the AI to work in a specific way, such as checklists or code review patterns.
What is the Model Context Protocol (MCP)? MCP is an open protocol that allows the model to access external data without exposing credentials directly in the prompt, encapsulating authentication through a server.
What is the role of Agents in AI orchestration? Agents act as orchestrators that think in logical loops, decide which Skill to apply, which MCP server to consult, and when to delegate tasks to other agents.
Is your pipeline orchestrated or improvised?
We bring the A-MAD framework, a diagnosis of your current stack and the path to separate Skills, MCP and Agents without rewriting everything.