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PRIMARY DISPLAY — COMMAND CENTER ACTIVE

AGENTIC AI

MISSION CONTROL

Command Center for Autonomous AI Systems

Agentic AI systems operate autonomously. They perceive their environment, set goals, plan multi-step actions, and execute with minimal human intervention. Unlike generative AI (reactive, prompt-dependent), agentic systems are proactive. They decompose objectives into sub-tasks, invoke external tools, maintain persistent memory, and adapt in real time. This hub covers how to understand, build, secure, and govern them.

Pillars

4

Frameworks

6

Standards

6

Threat Vectors

15

PANEL 01 Systems Overview ACTIVE

What Is Agentic AI?

AI systems that operate autonomously, perceive their environment, set goals, and execute multi-step plans without waiting for human prompts. Where a generative model asks “what should I create?”, an agentic system asks “what actions must I take to achieve this goal?”

Classification Autonomous AI

Capability Full-Spectrum

Risk Level Variable

Oversight Required

Loop Type Continuous

Tool Access Enabled

PANEL 02 Comparison Matrix LOADED

Generative AI vs. Agentic AI

Generative AI creates content on demand. Agentic AI executes goals autonomously. Here's how they compare across six operational dimensions.

Generative AI

ChatGPT DALL-E Midjourney Copilot

Autonomy

Memory

Tool Use

Planning

Content

Adapt

Agentic AI

Devin Claude Code Cursor AutoGPT

Autonomy

Memory

Tool Use

Planning

Content

Adapt

PANEL 03 Systems Architecture MONITORING

The Agentic AI Loop

The core operating cycle of every AI agent. Each cycle’s outputs feed the next, enabling self-correction and continuous improvement.

📡

Perception

Converts raw inputs (queries, APIs, sensors, logs) into structured representations via NLP and computer vision.

Active

🧠

Reasoning

LLM-powered cognitive core. Decomposes objectives into sub-tasks via Chain-of-Thought, Tree-of-Thought, and ReAct frameworks.

Active

🗃

Memory

Dual-layer: short-term working memory for task context, long-term persistent memory via vector embeddings for cross-session retrieval.

Standby

⚡

Action

Executes via structured JSON tool calls: remote APIs, headless browsers, code execution, and Computer Use Agents (CUAs).

Active

Continuous feedback loop // each cycle updates context for the next iteration

Agent Architecture Explorer

Trace how AI agents think, choose architecture patterns, and master the agentic loop

SCENARIO

ITERATION 1

PERCEPTION Step 1 of 8

Select a scenario and press Play to watch an agent think.

TOOL CALL

The agentic loop stays the same — but the flow between components changes based on the architecture pattern. Select a pattern to see how it reshapes the loop.

ReAct Pattern

Anthropic, OpenAI, LangChain

1 Perception Receive input

→

2 Reasoning Think + decide

→

3 Action Execute tool

→

4 Memory Store result

↺

BEST FOR General-purpose tasks, tool-use agents, chatbots with actions

RISK Unbounded loops — agent may cycle without converging

FRAMEWORKS LangChain, Claude Agent SDK, OpenAI Agents SDK, Google ADK

MATCH THE PATTERN 0 / 5

Loading question...

SOURCES: Anthropic "Building Effective Agents" (2024) | Yao et al. "ReAct" (2022) | Shinn et al. "Reflexion" (2023) | CSA MAESTRO Threat Taxonomy | OWASP Top 10 for LLM v2025 | NIST AI 100-1

PANEL 04 Sector Navigation 4 SECTORS ONLINE

SECTOR α — LEARN

Agent Architecture & Fundamentals

What is agentic AI, how does the agentic loop work, and what changed from generative to agentic paradigms.

DOCS LOADED: 7

STATUS: SYSTEM READY

What Is Agentic AI? From Chatbots to Autonomous Systems

The Agentic AI Loop: Perception, Reasoning, Memory, and Action

Generative AI vs. Agentic AI: What Changed and Why It Matters

SECTOR β — BUILD

Frameworks & Orchestration

Frameworks compared (LangChain, LangGraph, CrewAI, AutoGen), MCP deep dives, and cloud platforms (AWS Bedrock, Google ADK, Azure AI Agent Service).

DOCS LOADED: 8

STATUS: SYSTEM READY

LangChain vs. LangGraph vs. LlamaIndex: Choosing Your Agent Framework

Model Context Protocol (MCP): The Universal Agent Integration Layer

Cloud Agent Platforms: AWS Bedrock vs. Google ADK vs. Azure AI Agent Service

SECTOR γ — SECURE

Threats & Defenses

15 OWASP ASI threat categories, MITRE ATLAS adversary tactics, and CSA MAESTRO layer-based threat modeling for autonomous agent systems.

DOCS LOADED: 7

STATUS: ALERT LEVEL

The Agentic AI Threat Landscape: OWASP, MITRE ATLAS, and CSA MAESTRO

Prompt Injection in Agentic Systems: Why It's the #1 Threat

Tool Misuse, Excessive Agency, and the MCP Compositional Risk

SECTOR δ — GOVERN

Compliance & Risk

NIST AI RMF (Govern, Map, Measure, Manage), ISO 42001, EU AI Act high-risk classification, and Behavioral Bill of Materials (BBOM).

DOCS LOADED: 7

STATUS: MONITORING

The Agent Governance Stack: NIST AI RMF → ISO 42001 → EU AI Act

Behavioral Bill of Materials (BBOM): Documenting What Your Agent Can Do

EU AI Act and Agents: High-Risk Classification and Compliance Requirements

MISSION PROTOCOL — PANEL 05

Agent Blueprint Quest

5-question intake configures your deployment context. 8 levels of real agent architecture decisions. Each choice builds a live diagram. Scoring is context-adaptive: enterprise healthcare scores differently than startup prototyping. Output is a personalized deployment blueprint and printable certification.

Stages

8

Scenarios

24

Scoring

Adaptive

Output

Blueprint

01 Model Selection READY

02 Framework Selection READY

03 Memory Architecture READY

04 Tool Integration READY

05 Orchestration Pattern READY

06 Security Controls READY

07 Governance Layer READY

08 Deployment Strategy READY

PANEL 06 Deployment Scenarios 6 OPS

MISSION-001

Customer Support Ops

Multi-turn resolution agents with CRM integration and escalation protocols.

MISSION-002

Code Generation Wing

Autonomous coding agents that plan, implement, test, and iterate with human-in-the-loop review.

MISSION-003

Research Division

Deep research agents that synthesize across document collections and produce structured reports.

MISSION-004

Workflow Command

Automated approvals, cross-system data sync, and multi-agent delegation chains.

MISSION-005

Data Analysis Corps

Autonomous pipeline agents that ingest, clean, analyze, and surface insights from structured and unstructured data.

MISSION-006

Security Operations Center

Threat detection and response agents with SIEM integration, automated triage, and remediation playbooks.

PANEL 07 Threat Assessment ALERT

Threat Frameworks Monitored

Three agentic-specific security frameworks. The focus shifts from data protection to identity and access control.

OWASP ASI

15 Agentic Threat Categories (T1–T15)

MITRE ATLAS

ATT&CK-Style Tactics & Techniques for AI/ML

CSA MAESTRO

Layer-Based STRIDE Extension for Agents

Compliance Standards

NIST AI RMF

Govern • Map • Measure • Manage

ISO 42001

Certifiable AI Management System (2023)

EU AI Act

4-Tier Risk Classification (2024)

Agents operate as autonomous actors using Non-Human Identities (NHIs) with inherited permissions. OWASP ASI identifies 15 threat categories including memory poisoning, tool misuse, privilege compromise, and cascading hallucinations. These risks don’t exist in traditional LLM deployments.

PANEL 08 Armory — Agent Frameworks 6 LOADED

Six production-grade frameworks for building and orchestrating agentic systems. Different strengths for different deployment contexts.

LangChain

Python / TypeScript

Production Rapid Prototyping

LangGraph

Python / TypeScript

Production Stateful Graphs

CrewAI

Python

Production Role-Based Crews

AutoGen

Python

Experimental Microsoft

Semantic Kernel

C# / Python

Active Enterprise

Claude Agent SDK

Python / TypeScript

Emerging Anthropic

PANEL 09 Intelligence Brief 8 QUERIES

▶ QUERY-001: What distinguishes agentic AI from generative AI?

Generative AI creates content reactively. It waits for a prompt, produces output, and stops. Agentic AI pursues goals proactively. It decomposes objectives into sub-tasks, invokes external tools, maintains persistent memory, and adapts in real time. Generative AI is a “brilliant artist.” Agentic AI is an “autonomous project manager.”

▶ QUERY-002: What is the agentic loop and why does it matter?

Four interconnected subsystems running in a continuous cycle. Perception converts environmental data into structured representations. Reasoning decomposes goals into sub-tasks via CoT, ToT, and ReAct frameworks. Memory provides short-term task context and long-term persistent storage via vector embeddings. Action executes via JSON tool calls, APIs, or direct computer control. Each cycle feeds the next.

▶ QUERY-003: What are the biggest security risks for AI agents?

OWASP ASI identifies 15 threat categories: memory poisoning, tool misuse, privilege compromise, cascading hallucinations, goal manipulation, rogue agents, and more. The key difference from traditional LLM risks: a successful attack on an agentic system doesn't just produce bad text. It triggers autonomous cascading actions across enterprise systems.

▶ QUERY-004: Which agent framework should I choose for production?

LangChain for rapid prototyping (simple agent in under 10 lines). LangGraph for production (stateful graphs, durable execution, human-in-the-loop). CrewAI for role-based multi-agent crews. AutoGen for conversational collaboration (explicitly “not production ready” per Microsoft). Semantic Kernel for Microsoft enterprise stacks (C#/Python). Claude Agent SDK from Anthropic, emerging with Constitutional AI safety focus.

▶ QUERY-005: What is Model Context Protocol (MCP)?

Open-source standard from Anthropic that solves the M×N integration problem. Instead of custom connectors for every model-tool pair, MCP provides a single protocol (the “USB port for AI”). Three components: Host (your AI app), Client (translates LLM intent to JSON-RPC 2.0 requests), and Servers (lightweight adapters for databases, file systems, APIs). Enables dynamic tool discovery and secure credential management.

▶ QUERY-006: How does the EU AI Act apply to AI agents?

Four risk tiers: unacceptable (banned), high-risk (heavily regulated), limited risk (transparency obligations), minimal risk (no special rules). Most agentic deployments land in high-risk because they operate autonomously in critical domains. That triggers mandatory requirements for risk management, data governance, human oversight, and cybersecurity. NIST AI RMF (Govern, Map, Measure, Manage) provides a complementary voluntary framework to operationalize compliance.

▶ QUERY-007: What is a Behavioral Bill of Materials (BBOM)?

A documentation standard that catalogs everything an agent can do: tools, permissions, data access, decision boundaries, escalation rules, failure modes. Inspired by SBOMs in cybersecurity. When an agent is compromised, the BBOM defines the blast radius. Core question it answers: “What is this agent authorized to do, and what happens when it exceeds those boundaries?”

▶ QUERY-008: What is the Blueprint Quest and how does it work?

Interactive 8-level protocol where you make real agent architecture decisions. A 5-question intake sets your context (industry, scale, risk, team, compliance). Across 8 levels you choose between 3 options. Scoring is context-adaptive: what's optimal for enterprise healthcare differs from startup prototyping. Output is a personalized deployment blueprint and printable certification. Different intakes produce different paths.