The Agentic Paradigm Shift
A fundamental transformation is underway, moving from reactive AI tools to proactive, autonomous agents. This interactive report deconstructs the key architectural patterns driving this evolution. Explore the ecosystem below to understand how these new agentic systems reason, act, and learn.
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.
Click on an archetype to explore its architecture and capabilities.
Agent Archetypes: A Deep Dive
Each agent archetype represents a distinct axis of capability. Select a tab to explore how they work, from advanced information retrieval to direct interaction with computer interfaces.
Agentic RAG: From Augmentation to Agency
Agentic RAG transforms information retrieval from a static, one-shot lookup into a dynamic, reasoning-driven process. By embedding autonomous agents into the workflow, these systems can plan, execute multi-step searches, and iteratively refine results, mimicking a human research process.
Comparative Analysis
This section provides a centralized view of the key comparisons and data presented in the report. Use the dropdown to switch between different analytical tables and charts.
Future Frontiers & Critical Risks
The agentic paradigm is rapidly converging, but this progress introduces significant technical, security, and ethical challenges. Explore the future trajectory and the risks that must be managed for responsible development.
The Trajectory of Convergence
Specialized agent capabilities are merging. A future general-purpose agent will likely be a CUA at its core, incorporating the retrieval strategies of DR Agents and the execution power of CodeAct, all managed by a multi-agent orchestrator.
Continual Learning
Agents that improve their performance from ongoing interactions without full retraining.
Bootstrapped Reasoning
Multi-agent systems that create their own training data from successful task completions.
Self-Judging Models
Agents that evaluate their own outputs to generate reward signals for self-improvement.
Risk Landscape
Agent autonomy creates a new "cognitive security" attack surface. Click on a risk to learn more about its impact and potential mitigations.