The Agentic Paradigm Shift: An Interactive Analysis

The Agentic Paradigm Shift

A pivotal shift is happening, transitioning from reactive AI to proactive, self-governing agents. This dynamic report unpacks the core design principles fueling this change. Dive into the ecosystem below to see how these advanced systems think, decide, and adapt.

Agentic RAG

Dynamic, reasoning-driven information retrieval.

CodeAct Agents

Taking action through expressive, executable code.

Deep Research Agents

Automating complex, open-ended research tasks.

Computer-Using Agents

Interacting with the world via GUIs, mouse, and keyboard.

Agentic Core

Click on an archetype to explore its architecture and capabilities.

Agent Archetypes: A Deep Dive

Each agent type embodies a unique skill set. Choose a tab to discover their functions, from data retrieval to seamless computer interaction.

Agentic RAG: From Augmentation to Agency

Agentic RAG shifts information retrieval from static, single-step queries to a dynamic, reasoning-based approach. By integrating autonomous agents, it enables multi-step planning, iterative searches, and result refinement, closely resembling a human-like research workflow.

Traditional RAG

Agentic RAG

CodeAct: The Power of Imperative Action

The CodeAct approach transforms agent behavior, replacing rigid JSON tool calls with dynamic Python code generation. This empowers agents with unmatched versatility to integrate tools, manage intricate logic, and self-correct errors, all in one streamlined step.

JSON-Based (Multiple Turns)

CodeAct (Single Turn)

This example demonstrates how CodeAct streamlines complex data retrieval and computation into one seamless process.

Comparative Analysis

This part offers a unified overview of essential comparisons and data from the report. Select from the dropdown to toggle between various charts and tables.

Future Frontiers & Critical Risks

The agentic model is advancing swiftly, yet its growth brings complex technical, ethical, and security hurdles. Examine the path forward and the critical risks to address for mindful innovation.

The Trajectory of Convergence

Specialized agent functions are converging. Tomorrow's general-purpose agent may center on a CUA, blending DR Agents' retrieval techniques with CodeAct’s execution prowess, unified under a multi-agent orchestrator.

Continual Learning

Agents enhancing performance through continuous interactions, bypassing complete retraining.

Bootstrapped Reasoning

Systems of agents generating training data from completed tasks.

Self-Judging Models

Agents analyzing their outputs to derive rewards for enhancing performance.

Risk Landscape

Agent autonomy introduces a fresh 'cognitive security' risk landscape. Select a risk to explore its effects and possible countermeasures.