Beyond Automation: Securing Low-Code Agentic AI with MCP Guardrails

The rise of low-code/no-code (LCNC) platforms has made AI development faster and more accessible. Combined with agentic AI systems, which are autonomous AI components capable of executing multi-step tasks, the promise is powerful. However, beneath this efficiency lies a growing concern: what happens when these agents operate beyond our visibility?

This is where the Model Context Protocol (MCP) and secure scanning workflows come in. MCP provides a standardized interface for AI agents to interact with external tools, APIs, and environments, and scanning these connections becomes essential to maintaining trust, safety, and compliance.

Why MCP matters

MCP is used to allow AI models to communicate with external tools, data sources, and applications in a standardized way. This allows AI to perform tasks, access real-time information, and execute actions that go beyond its initial training data, making AI more useful and capable. I like to summarize this from two different lenses as follows:

1. For end users, it enables more powerful and context-rich AI-native (GenAI) applications, providing a better user experience. 
   

2. For enterprises, MCP fosters a standardized ecosystem, making it easier to maintain and extend LLM integrations across different systems.

In today’s low-code and no-code environments, it has become easy for both developers and business users to link multiple AI tools together to handle complex tasks – from generating code to connecting APIs and even deploying applications. These setups often rely on AI agents that make decisions using their own internal reasoning processes and external data. It’s a powerful capability, but with that autonomy comes a new set of risks.

Without guardrails, agentic systems can unintentionally expose sensitive data or execute insecure actions.

Visualizing MCP architecture (with scanner and guardrails)

Imagine the architecture as a layered stack where every agent action passes through embedded scanning and policy enforcement:

• MCP Request Handler plays a vital role in validating the intent and access of the task. 

• The MCP Scanner Layer acts as the core enforcement boundary, ensuring that any code, data, or command is secure before it’s executed, thereby minimizing the potential risk of vulnerable code.

• The Observability Layer ensures comprehensive traceability, with every system and model action immutably logged to support compliance, governance, and forensic analysis. 

Within the domain of non-deterministic AI models, such auditability serves as a foundational governance control. This enables accountability, reproducibility, and trust across the AI lifecycle.

The MCP scanning workflow explained

To address these unmitigated vulnerabilities, a framework around Toxic Flow Analysis (TFA) has emerged. TFA moves beyond simplistic prompt-level input validation and towards a deeper, contextual understanding of AI systems and their security posture. This approach offers the first comprehensive method to significantly reduce the attack surface of AI applications by mitigating indirect prompt injections and other MCP attack vectors.

TFA is a hybrid security analysis framework that seamlessly integrates static information, derived from an agent system's configuration, tool sets, and MCP servers, with dynamic runtime data captured from agents in production. This dual approach ensures proactive vulnerability detection and continuous monitoring. TFA preemptively predicts attack risks by constructing potential attack scenarios, leveraging a deep, contextual understanding of an AI system’s capabilities and susceptibility to misconfiguration.

At Snyk, we released an MCP scanner that can scan MCP configuration files using a command, as follows.

$ uvx mcp-scan@latest --full-toxic-flows

Beyond scanning: Observability and governance

It would be nice if security were just about blocking, but in the world of Agentic AI, equally important is visibility. A key advantage of MCP is its standardized interface, which allows organizations to apply consistent compliance and observability policies, automate audit processes, and maintain full transparency across the AI system's lifecycle. This includes: 

• Logging and tracing agent actions 

• Correlating decisions to data sources

• Enforcing policies centrally

• Integrating with existing DevSecOps pipelines where applicable 

When paired with continuous scanning, observability becomes the ultimate governance layer, allowing safe experimentation and scalable AI adoption

Conclusion 

As LCNC platforms evolve to include more autonomous AI behaviours, organisations need confidence that agents operate securely, not just efficiently.

By embedding MCP-based scanning workflows, enterprises can ensure:

• Every request is authenticated and analysed

• Every output is verified and compliant

• Every agent action is observable and enforceable

In short, the MCP scanning workflow becomes the invisible guardian of modern agentic systems, balancing agility with assurance in the age of autonomous AI. AI application security won’t be defined by who has the best scanner, but by systems intelligent enough to understand the code they protect.

As we continue to push the boundaries of intelligent application security, discover what’s possible withSnyk. Try MCP Scan today or explore Evo by Snyk.