Glossary AI & Vibecoding
Fundamental reference guide for the new era of software engineering by Andriy Orlov: autonomous agents, loop engineering, Model Context Protocol, context scaling, VPS infrastructure, and burnout prevention.
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ENAgent Benchmarks: GAIA and SWE-bench
A new generation of rigorous practical benchmarks for evaluating the autonomy of artificial intelligence. GAIA tests multimodal tasks in the open web with files and a browser, while SWE-bench assesses agents' ability to find and fix real bugs in large GitHub repositories.
Agent Evals & SWE-bench Benchmarking
A methodology and infrastructure for systematically measuring the reliability, accuracy, and safety of AI agents through synthetic tests, SWE-bench, and headless repository simulations.
Agent Handoffs & State Transfer
A standardized pattern for the secure transition of tasks and context from one specialized AI agent to another without losing goals, history, and accumulated artifacts.
Agent Identity & DID (Decentralized Identifiers)
Cryptographic standards (DIDs, Verifiable Credentials, mTLS) that provide autonomous AI agents with a legally and technically recognized identity, signing rights, and action auditing.
Agent Memory
A comprehensive subsystem for data storage, filtering, and retrieval that transforms stateless LLM calls into a stateful system: from short-term scratchpad buffers to multi-session knowledge repositories.
Agent Observability & Tracing (OpenTelemetry)
Methods for collecting metrics, tracing reasoning chains (Spans), analyzing tool latencies, and monitoring token costs using OpenTelemetry and specialized platforms (Langfuse, Arize).
Agent Sandboxing
Hardware and software isolation of an autonomous agent's execution environment, ensuring the protection of the host system, secrets, and internal network from malicious code and prompt injection.
Agent Scratchpad
A temporary working memory area within the model's context window where the agent records its intermediate thoughts, plans, doubts, and conclusions before invoking tools. Hidden from the end user to maintain the cleanliness of the final interface.
Agent Swarms & Consensus Voting
Decentralized coordination of a large number of homogeneous or heterogeneous AI agents, where final decisions are formed through majority voting, debates, or consensus algorithms.
Agent-to-Agent Protocol (A2A)
An open standard for network interaction, decentralized discovery, and secure context exchange between independent AI agents across various vendors and platforms.
Agentic Loop: Steps of Thought ➔ Action ➔ Observation (ReAct)
A fundamental algorithmic pattern for autonomous agents, known as ReAct: Reasoning + Acting, consisting of an infinite cycle of three steps: 1) Thought — situation analysis; 2) Action — tool invocation; 3) Observation — result analysis and plan adjustment.
AI Agents (Autonomous Agents)
An autonomous system based on a large language model that not only responds to messages but independently plans a sequence of actions, utilizes external tools (browser, terminal, databases), and executes complex tasks without constant human oversight.
AI Agents (Autonomous Agents)
Software systems based on LLMs that can autonomously perceive the state of the environment, decompose complex goals, invoke external tools, and iteratively correct their own mistakes.
Autonomous Browser & Computer Use
Multimodal technology for controlling graphical user interfaces (GUIs) through visual perception of screenshots, cursor emulation, clicks, and keyboard input without using APIs.
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ENC
ENChatGPT Memory (Storing Facts Between Dialogues)
The ChatGPT Memory feature enables the AI to retain your name, profession, favorite technologies, response formats, and personal details across entirely different chats, enhancing user experience and personalization.
Computer Use via AI
Anthropic's technology (introduced in Claude 3.5 Sonnet) enables neural networks to control operating systems like a human: taking screenshots, analyzing window coordinates, moving the mouse cursor, clicking buttons, and entering text without using specialized APIs.
CrewAI
One of the most popular Python frameworks for creating autonomous teams of agents, based on role distribution of responsibilities, tools, and task delegation.
Custom GPTs
ChatGPT's feature for creating personalized mini-assistants tailored to specific tasks. It allows uploading custom files, setting persistent instructions, integrating image generation, and connecting external APIs without coding.
D
ENDeterministic Tool Calling & Grammar Sampling
This technology ensures 100% syntactical validity of agent tool arguments through logit grammar masking (GBNF / Outlines) and strict validation using Pydantic/Zod schemas.
Dynamic Tool Selection
An architectural approach for building scalable AI agents equipped with hundreds of tools. Instead of loading all function descriptions into the context simultaneously, the system employs semantic search or a Router model for dynamic selection of only 3-5 most relevant tools for a specific user query.
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ENG
ENH
ENHermes Agent & Hedonist Orchestrator
A sovereign agent ecosystem by Andriy Orlov that transforms personal Telegram into a full-fledged control panel for Linux VPS, multimodal pipelines, and autonomous workflows.
Human-in-the-Loop (HITL) Approval Gates
The Human-in-the-Loop (HITL) architectural security pattern establishes Approval Gates before executing critical actions. The agent prepares an operation (payment, database deletion, sending an email to a client) but pauses until explicit approval from a human.
I
ENIndirect Prompt Injection Defense
A comprehensive set of architectural and software methods to protect autonomous AI agents from hidden instructions placed by attackers in external web pages, documents, or APIs.
Infinite Loops in Agents and Budget Protection
A critical state of an autonomous agent where it enters an infinite recursive action loop due to a recurring error or inability to achieve its goal. This entry discusses wallet protection mechanisms: strict step limits (Max Steps), spend caps, and loop detection.
Interrupt-Driven HITL & Breakpoints
A design pattern for agent systems that allows for pauses, human intervention, and safe state resumption before executing critical actions.
L
ENLangChain Framework
The most popular open-source framework (available in Python and TypeScript) for building applications based on large language models. It provides hundreds of ready-made modules to integrate LLMs with documents, vector databases, API tools, and memory (Chains).
LangGraph
A low-level framework from the LangChain team for building deterministic, cyclic multi-agent systems as finite state machines with full persistence support.
M
ENMCP (Model Context Protocol)
An open standard from Anthropic based on JSON-RPC 2.0 for unified bidirectional connection of AI assistants to external tools, databases, and system environments.
MCP Client
A software environment (Claude Code, Cursor, Cline, SDK agents) that manages the lifecycle of connections to MCP servers, aggregates tool manifests, and controls model access rights.
MCP Server
A software service or background process that implements the MCP specification, providing external AI clients with standardized access to function execution, resource reading, and prompt templates.
Microsoft AutoGen
A leading open framework from Microsoft for creating multi-agent conversational systems. It enables the integration of multiple AI agents with different roles and skills into a GroupChat, where they discuss, write code, test it, and collaboratively achieve business goals.
Monte Carlo Tree Search for Agents (MCTS)
An algorithmic approach to AI agent action planning that combines heuristic search, intermediate state evaluation, and backtracking to find optimal solutions in high-complexity spaces.
Multi-Agent Orchestration
An architecture for the interaction of independent specialized AI agents, united in a distributed network or hierarchy to solve complex engineering tasks in parallel.
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ENPlan-and-Solve Prompting
A two-stage agent architecture that separates the strategic decomposition of a task into a global plan from its sequential tactical execution with dynamic replanning.
Process Reward Models (PRM)
AI evaluation models that analyze the correctness of each individual logical step or agent tool invocation, preventing the accumulation of errors before reaching the final outcome.
PydanticAI
A modern Python framework from the creators of Pydantic that introduces strict typing, Dependency Injection, and deterministic schema validation into the realm of AI agents.
R
ENReAct Pattern (Reasoning + Acting)
A fundamental algorithmic pattern for autonomous agents that alternates between internal reasoning steps (Thought), executing external tools (Action), and analyzing the resulting output (Observation).
Reflection Pattern
An architectural pattern that enhances agent reliability by dividing the process into solution generation (Generator), critical auditing (Critic), and iterative refinement (Refiner).
RLVR (Reinforcement Learning with Verifiable Rewards)
A post-training method for optimizing AI agent reasoning, where the reward function is based on objective mathematical verifications, compilers, and unit tests instead of subjective human evaluations.
S
ENSelf-Correction Loop
A mechanism for autonomous code correction by the model through receiving grounded feedback from compilers, linters, or tests.
Self-Healing Code & Runtime Loops
An autonomous engineering loop where an AI agent modifies code, analyzes compiler feedback and runtime logs, and iteratively resolves its own errors until achieving 100% functionality.
Short-Term and Long-Term Memory of Agents
The architecture of multi-tier memory for autonomous agents. It distinguishes between Short-Term Memory (active context window of the current session) and Long-Term Memory (vector databases, user profile files, and episodic diaries).
Subagent Delegation Architecture
A hierarchical architectural pattern for building complex AI systems. Instead of attempting to solve everything with a single universal language model, a main orchestrator agent (Router/Manager) breaks down tasks and delegates them to specialized subagents (Researcher, Coder, Critic).
Subagents and Delegation
An architectural pattern for launching ephemeral isolated child agents to execute resource-intensive subtasks in parallel without polluting the parent process's context window.
Supervisor Pattern (Hierarchical Multi-Agent)
An architectural template for organizing AI agents, where a central supervisor agent manages the lifecycle, task decomposition, and delegation to a pool of specialized workers.
Synthetic Personas & Multi-Role Simulation
A methodology for modeling diverse user roles, security auditors, and accessibility experts using specialized system prompts for comprehensive stress testing of applications.
T
ENTool Calling (Function Calling)
A low-level mechanism in language models that enables them to reliably generate validated parameters in JSON format for executing functions in external programming environments.
Tool Schemas (Tools & JSON Schema)
A standardized formal description of tool interfaces for large language models using the JSON Schema standard. It includes the function name, a detailed textual description of its purpose, a list of required parameters, and their value types.
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ENFull Stack of Tools, Tier Lists, and AI Skills
Beyond the glossary, GOTBURNOUT provides frontier AI benchmarks, a catalog of over 100,000 AI Agent Skills, production-ready VPS hardening scripts, and an exclusive Pro developer community.