Steer AGI

"To solve really hard problems, we'll have to use several different representations."

"Artificial intelligence will be the last invention humanity will ever need to make."

Accuracy

"The correctness of decisions and actions taken by AI agents, validated through continuous learning and feedback mechanisms."

Agent Customization

"Tailoring agents to specific tasks through parameter adjustments or specialized training."

Agent Development

"The process of creating agents with modules for perception, cognition, and action execution."

Agent Interaction

"Communication between agents via shared memory or protocols to coordinate actions."

Agent Memory

"A repository storing short-term (immediate context) and long-term (historical data) information for decision-making."

Agent Prompt

"Instructions guiding an agent’s behavior within specific contexts or tasks."

Agentic AI

"Autonomous systems that perform tasks with minimal human intervention by integrating perception, planning, and action."

Agentic Framework

"A structured architecture enabling agents to autonomously interact with environments and tools."

Agentic Patterns

"Reusable design strategies for building goal-oriented agents, such as multi-step reasoning or collaboration."

Agentic RAG

"Combines retrieval-augmented generation (RAG) with autonomous decision-making for context-aware responses."

Agents

"Autonomous entities that perceive environments, set goals, and execute actions."

AI Agent Collaboration

"Coordination among multiple agents via shared memory or communication protocols to achieve common objectives."

Alignment

"Ensuring agent behavior aligns with ethical guidelines or predefined objectives."

Autonomous Operation

"Goal-driven execution of tasks without constant human oversight."

Cognitive Architecture

"A blueprint for agent design, integrating perception, reasoning, and action modules."

Collaboration

"Agents working together through shared goals and coordinated plans."

Concept-CoT Agent

"An agent using chain-of-thought reasoning to break down abstract concepts into actionable steps."

Continual Pretraining

"Ongoing training of models on new data to maintain relevance and adaptability."

CoT (Chain-of-Thought)

"A reasoning method where agents decompose problems into sequential steps."

Design Patterns

"Reusable solutions for common challenges in agent architecture, like coordination or error handling."

Distillation

"Compressing complex models into smaller, efficient versions while retaining core capabilities."

Functional Calling

"The ability of agents to invoke external tools or APIs during task execution."

Goal

"The objective an agent aims to achieve, guiding its planning and actions."

HITL (Human-in-the-Loop)

"Human oversight for validation, correction, or ethical compliance in agent operations."

Improvement Over Time

"Agents refining performance through learning algorithms like RLHF or supervised fine-tuning."

Logicality

"Coherent and consistent reasoning processes within agents."

Long-term Memory

"Persistent storage of historical data for informed decision-making."

LRM

"Language Reasoning Model (context-specific term; possibly a variant of LLM)."

MAS (Multi-Agent Systems)

"Networks of agents collaborating to solve complex problems."

MCP

"The Model Context Protocol (MCP) is an open-source standard developed by Anthropic to simplify and standardize how large language models (LLMs) interact with external data sources and tools. MCP enables seamless integration by providing a universal interface, eliminating the need for custom integrations, and allowing AI applications to access context-rich data efficiently through a client-server architecture using JSON-RPC communication"

Model Outputs

"Structured or unstructured results generated by agents, such as decisions or data."

MoE (Mixture of Experts)

"Architecture where specialized submodels handle distinct tasks."

Multi-Agent CoT Prompting

"Coordinated chain-of-thought reasoning across multiple agents."

Multi-Agent Conversations

"Interactions between agents using natural language to negotiate or collaborate."

Multi-Agents

"Systems where multiple agents interact, each with specialized roles."

Multi-step Processes

"Tasks requiring sequential planning and execution across interdependent steps."

Open-Ended Problems

"Challenges without predefined solutions, requiring adaptive reasoning and creativity."

Orchestration

"Managing agent workflows, tool usage, and resource allocation."

Post-Training

"Techniques like fine-tuning applied after initial model training to enhance performance."

Procedural Memory

"Storage of learned skills or processes for task execution."

Prompt Template

"Predefined structures guiding agent responses or actions in specific scenarios."

RAG (Retrieval-Augmented Generation)

"Enhancing responses with external data retrieval for accuracy."

RAG-powered Contextual Understanding

"Using retrieved data to inform real-time decisions."

ReAct (Reasoning and Acting)

"A framework where agents alternate between reasoning and taking actions."

Reasoning

"Processing information to derive insights, often using LLMs for logical inference."

Reflection

"Agents analyzing past actions to improve future decisions."

Reinforcement Learning

"Training agents via rewards/penalties to optimize behavior."

RLHF (Reinforcement Learning from Human Feedback)

"Aligning agent behavior with human preferences through feedback."

Short-term Memory

"Temporary storage of immediate context for real-time decision-making."

Structured Outputs

"Formatted results (e.g., JSON or tables) ensuring consistency in agent responses."

Supervised Fine-Tuning

"Refining pre-trained models using labeled data for specific tasks."

System Prompt

"High-level directives defining an agent’s role or operational boundaries."

Tools

"External resources (APIs, databases) agents use to execute tasks."

Workflows

"Sequences of automated steps agents follow to accomplish complex tasks."