Review: Agentic AI security
A Simple Walk through of Agentic AI Architecture.
This post, we will try to show more about Agentic AI Architectures, but first let’s talk about Agents. Also, you might want to check out our previous post:
A few examples of agents:
Planning Agent: Multi-step task execution.
RAG-Based Agent: Use external info on demand.
Hierarchical Agent: Delegate work to sub-agents.
Human-in-the-Loop Agent: Rely on user confirmation at key steps.
🧱 Why This Matters for Security
Understanding the architecture lets you map threats more accurately:
Memory Poisoning → Memory Layer.
Tool Misuse → Function Interface.
Identity Spoofing → Agent Communication.
Privilege Compromise → Role/Permission Management.
When you see the system as a series of layers and communication channels, it becomes easier to:
Model risk.
Apply mitigation.
Monitor effectively.
Let’s see review architecture
Example: DecipherIt — AI Research Assistant, powered by CrewAI + Bright Data + full-stack precision (More please scroll down in the reference [3])
Here’s what it can do:
📄 Upload docs
🔗 Add URLs
💬 Ask questions
📊 Auto-summarize
🧭 Visualize ideas as mindmaps
🎧 Generate audio overviews
🌐 Scrape the global web (yes, even past bot detection)
Behind the scenes?
A crew of agents coordinating end-to-end research:
🤖 Researcher, Scraper, Writer, FAQ Generator, Mindmap Builder, Script Creator
🧩 Powered by:
• CrewAI for multi-agent orchestration
• Bright Data MCP for geo-unlocked web scraping
• Qdrant + OpenAI for vector Q&A
• Lemonfox for AI voiceover
• FastAPI backend + Next.js + React 19 frontend
🧠 What Makes an AI Agent "Agentic"?
Agentic AI isn’t just about generating answers—it’s about acting on them. But what does that mean from a system design perspective?
🏗️ Single-Agent Architecture: The Core Building Blocks
An Agentic AI system typically consists of the following components:
1. Application Layer
This is your main interface. It could be a chatbot, API, or UI where the user interacts.
Often built using frameworks like LangChain, AutoGen, or CrewAI.
2. Reasoning Engine
Usually powered by a large language model (LLM) like GPT-4, Claude, or open-source LLMs.
This is where planning, decision-making, and tool selection happens.
3. Tool/Function Interface
Enables the agent to perform actions: calling APIs, querying databases, triggering scripts.
May include built-in functions or external services via plugins.
4. Memory (State Management)
Short-term: Session-based memory
Long-term: Vector databases, file storage, persistent memory across sessions
5. External Resources
This includes access to files, internet search, company knowledge bases, etc.
Commonly used in Retrieval-Augmented Generation (RAG) workflows.
🧑🤝🧑 Multi-Agent Architecture = Teams of AI Working Together
When one agent isn’t enough, systems can orchestrate multiple agents with different roles. Think of it as an AI team, with a supervisor and specialists.
What’s New:
Inter-agent communication
Task delegation between agents
Coordination via a manager/scheduler agent
Example:
One agent handles planning.
Another executes tasks.
A third validates results.
This model boosts power and flexibility but introduces new challenges like:
Communication poisoning.
Privilege escalation via agent delegation.
Rogue or compromised agents.
So, what is the main difference between Agents and Agentic AI workflow:
📐 Agentic AI/ Agent Patterns You Should Know
Agentic behaviors can follow certain common patterns. Understanding them helps in designing and threat-modeling your systems.
Reference
[1] OWASP, Agentic-AI-Threats-and-Mitigations_v1.0a
[2] AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges, https://arxiv.org/abs/2505.10468
[3] Decipherit.xyz, https://github.com/mtwn105/decipher-research-agent