How to Build and Secure Agentic AI Systems

Artificial intelligence (AI) systems continue to reshape industries, with agentic AI systems standing out as a particularly transformative innovation. These systems go beyond simple automation by dynamically interacting with data, tools, and environments to generate intelligent responses and execute tasks. However, alongside their potential, agentic AI systems also present unique challenges, with security being paramount.

This article, inspired by a detailed session by Sarab Satish, CTO of Pangia, explores how to build, secure, and maximize the potential of agentic AI systems. Whether you're a business leader exploring AI integration or a technology enthusiast delving into its technical depths, this guide provides actionable insights to help you navigate the complexities of agentic AI.

What Are Agentic AI Systems?

Agentic AI systems are specialized software programs that leverage large language models (LLMs) to interact with external environments, retrieve information, and execute tasks based on user queries. Unlike traditional AI workflows, which rely on static inputs and outputs, agentic AI systems dynamically orchestrate workflows and collaborate with tools, databases, and sometimes other AI agents to deliver actionable results.

Key components of an agentic AI system include:

• Interaction with LLMs: Using LLMs for reasoning, planning, and decision-making.
• Prompts: Structured instructions that guide the LLM's problem-solving process.
• Memory: Short-term and long-term storage for context and historical data.
• Tool Integration: The ability to call tools or APIs to execute specific actions.

These systems are especially powerful in enterprise environments, where they enhance real-time decision-making, automate workflows, and generate highly contextualized outputs.

Building Agentic AI Systems: Core Concepts and Architecture

1. Understanding LLMs and RAG Architectures

LLMs are foundational to agentic AI systems, providing the ability to analyze input, generate context-based outputs, and guide agents. However, it's important to recognize their limitations:

• Static Knowledge: LLMs are trained on data up to a specific point and lack real-time access to information.
• Context Windows: The working memory of an LLM determines how much data it can process in real-time.

Retrieval-Augmented Generation (RAG) architectures address these challenges by augmenting LLMs with data retrieved from external sources (e.g., vector databases). Agentic RAG takes this further by enabling agents to interact with live environments, retrieve data, and execute tasks.

2. Core Workflow of Agentic AI

An agentic AI workflow follows a structured process:

• User Query: A user inputs a question or request.
• LLM Planning: The LLM breaks down the query into subtasks and determines the tools needed.
• Tool Invocation: The agent calls the appropriate tools to retrieve or process data.
• Result Compilation: Data from tools is fed back into the LLM, which continues the process until a final answer is produced.

3. Integrating Modular Tooling with MPC

Multi-Party Computation (MPC) introduces modularity to agentic systems by separating tool implementation from the agent itself. Instead of hardcoding tools, they are hosted on external MPC servers, allowing for:

• Dynamic Tool Discovery: Agents dynamically retrieve available tools.
• Ease of Maintenance: Tool updates and changes do not require agent reconfiguration.
• Scalability: Tools can be shared across multiple agents.

Security Challenges in Agentic AI Systems

While agentic AI systems offer unparalleled capabilities, they also introduce unique security risks. Key challenges include:

1. Privilege Escalation

Agents often operate with elevated privileges to serve users with varying levels of access. If not carefully designed, users could exploit agents to perform unauthorized actions.

2. Input Manipulation

Agents process external data, making them susceptible to malicious inputs. Poorly sanitized data can lead to unintended behaviors or vulnerabilities.

3. Credential Leakage

Agents interacting with external systems often require access tokens and credentials. Improper management of these credentials can lead to accidental exposure.

4. Memory Exploitation

Agents with memory capabilities risk exposing sensitive information from prior interactions to unauthorized users.

5. Denial of Service (DoS) Risks

Bugs, loops, and excessive tool invocation can overload systems and APIs, leading to denial of service.

Best Practices for Securing Agentic AI Systems

1. Adopt a Modular Design with MPC

Separating tools from the agent reduces the risk of hardcoded vulnerabilities. MPC-based modularity also makes it easier to monitor and secure individual components.

2. Implement Principle of Least Privilege

Agents should only have access to the tools, data, and resources necessary to fulfill their defined tasks. This reduces the attack surface.

3. Harden System Prompts

System prompts should enforce constraints that keep the agent focused on legitimate tasks. For example, prompts can restrict the agent from accessing certain tools or data.

4. Sanitize Inputs and Outputs

All data sent to the LLM and returned by tools should be checked for malicious content, sensitive information, and injection attacks.

5. Monitor and Audit

Use logging and monitoring solutions to track agent activity, tool usage, and user interactions. This helps identify anomalies and potential breaches.

Advanced Security Risks and Mitigations

Tool-Specific Threats

1. Tool Poisoning: Malicious actors may create tools that perform unintended actions, such as extracting confidential data. Mitigation: Vet and test all tools before integration.
2. Rug Pull Attacks: Tools may evolve over time to include malicious actions. Mitigation: Regularly review external dependencies and maintain version control.
3. Tool Shadowing: Tools can influence how other tools are used, introducing malicious parameters. Mitigation: Use robust input validation and strict tool dependencies.

Guardrails and Risk Mitigation Strategies

1. Prompt Injection Detection: Employ guardrails to identify and block injection attempts that exploit LLM behavior.
2. Content Moderation: Monitor for sensitive data or inappropriate topics in agent interactions.
3. Authorization Enforcement: Ensure tools align with user permissions, preventing unauthorized access to sensitive data or actions.

Key Takeaways

• Agentic AI Systems: These systems provide dynamic, real-time problem-solving by orchestrating workflows with LLMs, tools, and external environments.
• Security Is Crucial: Ensure agents follow the principle of least privilege, sanitize inputs/outputs, and implement strong guardrails.
• MPC Modularity: Modular architectures reduce complexity, improve scalability, and facilitate security in tool implementation.
• Unique Risks: Be aware of threats like tool poisoning, rug pull attacks, and tool shadowing, and design systems to mitigate them.
• Prompt Engineering: A well-designed system prompt ensures effective LLM behavior, improving resilience, error handling, and security.
• Guardrails Matter: Leverage built-in LLM guardrails, open-source tools, or commercial solutions to protect your systems from emerging threats.
• Memory Management: Design memory systems to avoid leaking sensitive information across user sessions.
• Test and Monitor: Regular testing and robust monitoring provide visibility and ensure consistent behavior.

Closing Thoughts

Agentic AI systems offer transformative potential for enterprises and technical teams, but their benefits come with challenges that demand careful attention. By adopting modular architectures, integrating robust security measures, and leveraging appropriate guardrails, organizations can harness these systems safely and efficiently.

AI is evolving rapidly, and so are its risks. Stay informed, prioritize security, and continue innovating responsibly to unlock the full potential of agentic AI in your organization. Whether you're building from scratch or leveraging frameworks, the key lies in balancing functionality with safety. Happy building!

Source: "Building & Securing AI Agents: A Tech Leader's Crash Course" - Pangea, YouTube, Aug 5, 2025 - https://www.youtube.com/watch?v=B_fMI97AsPc

Use: Embedded for reference. Brief quotes used for commentary/review.

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