AGENTIC AI GLOSSARY · GUIDED LEARNING · REVIEWED SOURCES
Agentic AI Glossary
Understand agentic AI without fighting the vocabulary. Ask a question in everyday language, look up one of 721 reviewed concepts, or follow a guided path from foundations to governed multi-agent systems.
Explore the ecosystem and inspect how the answers are governed.
The advanced tools are still here, but they no longer compete with the first thing most visitors need: a clear answer or a clear lesson.
Local system & progress receiptsOptional diagnostics for privacy, local inference, and deterministic replay
Local searchPreparing
MiniLM proposals remain same-origin and deterministic authority remains in OverLift.
Learning progress receiptCreated after the Learning Center opens
No account, server write, or external credential.
01 · CURRENT ECOSYSTEM
Products & frameworks
Compare reviewed APIs, frameworks, runtimes, platforms, coding agents, and evaluation tools without collapsing their differences.
02 · VISUAL CONNECTIONS
Concept map & search trace
Follow typed relationships or watch CATS, Tier-S, and the observer-only Quantum Lens trace a real question.
03 · SAFE FAILURE
Safety Lab
Run bounded failure challenges and see how unsupported, stale, malformed, or unsafe paths stop instead of being forced through.
04 · ENGINEERING PROOF
How search works
Inspect candidate generation, deterministic authority, selected and admitted paths, receipts, replay, and fail-safe fallback.
Inspect how this answer was selectedCATS route, Tier-S admission, candidate evidence, receipt, and replay
CATS routeT0 · Exact identity
Query familyGeneral discovery
Executed queryBrowse all terms
Typed filtersNone
Why this pathCATS is preparing the least expensive qualified path.
Active engineDeterministic fallback
T0Exactwaiting
T1Ready pathwaiting
T2Lexicalwaiting
T3Graphwaiting
T4Wasmwaiting
T5Cachewaiting
T6Semanticwaiting
T7Explainwaiting
Inspect rankingSee the typed plan, corrections, field evidence, scores, and receipt
Original question
None
Normalized input
none
Executed query
browse all terms
Family
General discovery
Corrections
None
Alias expansion
None
Deterministic rewrite
None
Semantic coverage
4792 questions · 4026 alternate phrases
Filters
None
CATS route
T0 · Exact identity
Executor
Deterministic fallback while CATS prepares
Engine
Deterministic JavaScript
Eligible paths
T0
Withheld paths
T1–T7 until qualified
Cache
No sealed result reused
CATS configuration
waiting
Tier-S verification
Waiting to shadow-check the selected route.
Receipt
waiting
Top admitted candidatesStable score and visible evidence
Generative AI & Language Models
Vocabulary
industry
vocabulary
Entry type
concept
Publisher
Lifecycle
concept
Reviewed
2026-08-14
Delivery
Plain-language answer
The fixed set of token units a tokenizer and language model can represent directly.
Why it matters
It explains what generative models can contribute while preserving the difference between fluent output, evidence, and proof.
Example
A generated explanation can use this concept, but its claims still need admitted evidence and a receipt before they are trusted.
Common confusion
Fluent or high-probability output is not proof that a claim is factual or grounded.
How OverLift uses it
OverLift teaches Vocabulary as an adjacent industry or mathematical concept so visitors can compare it with the current architecture. Its presence in the glossary is not a claim that every variant is implemented.
Explore the technical detailFormal definition, lifecycle, use boundaries, capabilities, prerequisites, and comparisons
Technical definition
Within a generative-language pipeline, Vocabulary is the formal concept for the fixed set of token units a tokenizer and language model can represent directly.
Move from one definition into Sean Findley’s product and engineering proof.
NO SUPPORTED GLOSSARY MATCH
No answer was admitted for that search.
The glossary could not connect this wording to a reviewed canonical term. It is showing no answer rather than forcing an unrelated concept.
AGENTIC AI LEARNING CENTER · POWERED BY THE OVERLIFT METHOD
Build one connected idea at a time.
Choose a track, finish one focused lesson, and continue when the concept is clear. Comparisons and checkpoints stay available without crowding the lesson.
Completed0 / 32 lessons
Progress0%
Your progressPrivate to this browser · no account required
0x00000000All learning tracks8 guided paths · open only when you want to switch
ACTIVE TRACK
Preparing Learning Center
The reviewed lesson pack is verified only after you open this workspace.
Loading the selected lesson…
↔Compare two ideas
See one practical distinction without leaving this lesson.
›
COMPARE WITHOUT COLLAPSING THE DIFFERENCE
Eight practical distinctions
Each comparison stays tied to canonical glossary terms and can be sent through governed search.
✓Check what you learned
Complete a four-question, source-backed checkpoint.
›
SOURCE-BOUND CHECKPOINT
Test the active track
Four questions. Explanations point back to the exact concepts. A score is local evidence of practice, not an external credential.
CURRENT PRODUCTS · FRAMEWORKS · RUNTIMES · PLATFORMS
Understand what each product actually is before comparing features.
These profiles separate APIs, open-source frameworks, managed runtimes, low-code builders, enterprise platforms, evaluation products, and coding agents. Lifecycle status and official sources are visible instead of being buried in marketing language.
Showing all 39 source-reviewed profilesCurrent means reviewed against official product material on the date shown—not permanently current and not independently certified.
AG2
AG2
current
frameworkOpen-source Python framework
An open-source programming framework for conversational agents, multi-agent networks, tools, human collaboration, structured output, and evaluation.
An AWS platform for building, deploying, operating, securing, connecting, remembering, observing, and evaluating agents using different frameworks and models.
runtimememoryidentitygateway
Amazon Web Services
Amazon Bedrock Agents Classic
maintenance
managed platformManaged service in maintenance mode
The earlier managed Bedrock agent service for orchestrating foundation models, action groups, knowledge bases, and user requests.
A Python and TypeScript SDK that exposes the agent loop, tools, and context-management capabilities used by Claude Code.
agent looptool permissionssessionshooks
Anthropic
Claude Code
current
productTerminal, IDE, desktop, browser, and SDK-backed product
An agentic coding tool that reads codebases, edits files, runs commands, and works through development tasks in terminal, IDE, desktop, and browser surfaces.
coding platformManaged and self-managed GitLab platform
GitLab's AI-native platform for agentic chat, foundational and custom agents, flows, sessions, tools, and asynchronous work across the software lifecycle.
agentic chatfoundational agentscustom agentsflows
Google Cloud
Agent Development Kit
current
frameworkOpen-source Python and Java framework
Google's open-source framework for building, debugging, evaluating, and deploying agents and multi-agent systems across models and tools.
agentstoolsmulti-agent systemssessions
Google Cloud
Agent Platform Runtime
current
managed runtimeManaged runtime service
The managed execution layer within Google's agent platform for running, scaling, isolating, and operating deployed agents and supporting services.
managed executionscalingsessionsmemory
Google Cloud
Gemini Enterprise Agent Platform
current
managed platformManaged Google Cloud platform
Google Cloud's unified platform for building, deploying, governing, integrating, and optimizing enterprise-grade agents and model-based applications.
agent buildingmanaged deploymentidentitygateway
Hugging Face
smolagents
current
frameworkOpen-source Python library
A compact open-source Python library for building agents with code-generation or structured tool-calling execution styles.
code agentstool-calling agentsMCPmodel adapters
IBM
IBM watsonx Orchestrate
current
enterprise platformManaged IBM enterprise platform
IBM's enterprise platform for building, cataloging, connecting, orchestrating, deploying, and using agents, tools, applications, and MCP servers.
agent buildercatalogtoolsMCP servers
LangChain
Deep Agents
current
agent harnessOpen-source agent harness
A batteries-included agent harness with planning, subagents, filesystem tools, context management, and long-running execution on LangGraph.
frameworkOpen-source .NET, Python, and Go framework
Microsoft's multi-language framework for building agents and explicit multi-agent workflows with state, middleware, telemetry, checkpoints, and human input.
agentsgraph workflowscheckpointshuman input
Microsoft
Microsoft Copilot Studio
current
low code platformManaged low-code platform
A graphical low-code studio for building and managing agents, workflows, agent flows, tools, knowledge connections, and publishing channels.
low-code agentsworkflowsagent flowsconnectors
Microsoft
Microsoft Foundry Agent Service
current
managed platformManaged Microsoft Foundry service
A managed Microsoft Foundry service for building, hosting, deploying, observing, and scaling agents across supported frameworks and models.
managed agent hostingtool catalogidentity and networkingtracing
Microsoft
Semantic Kernel
transition
frameworkOpen-source SDK
An open-source Microsoft SDK for integrating models, prompts, plugins, memory, and agent patterns into .NET, Python, and Java applications.
model connectorspluginsfiltersagents
MLflow and Databricks
MLflow for GenAI
current
evaluation platformOpen platform with managed Databricks capabilities
An open platform for tracing, evaluating, monitoring, reviewing, and improving GenAI applications, RAG systems, and agents across development and production.
tracingevaluationscorershuman feedback
NVIDIA
NVIDIA NeMo Agent Toolkit
current
toolkitOpen-source Python toolkit
A framework-agnostic toolkit for connecting, profiling, observing, evaluating, and optimizing enterprise agent workflows across existing agent frameworks.
framework integrationtoolsprofilingobservability
OpenAI
OpenAI Agents SDK
current
frameworkOpen-source Python SDK
A lightweight software development kit for building tool-using agents, handoffs, guardrails, sessions, and traced agent runs.
agents and runnersfunction toolshandoffsguardrails
OpenAI
OpenAI Codex
current
productHosted product, app, CLI, IDE, and web
A software-engineering agent that can inspect repositories, plan work, edit files, run commands, and complete coding tasks across several interfaces.
Salesforce's platform for creating, customizing, testing, governing, and deploying business agents across Salesforce data, workflows, and channels.
business agentsSalesforce dataactionstesting
ServiceNow
ServiceNow AI Agent Orchestrator
current
orchestration runtimeManaged ServiceNow orchestration service
ServiceNow's central orchestration component for planning, selecting, coordinating, and supervising agents that collaborate on complex platform workflows.
planningagent selectiondelegationcoordination
ServiceNow
ServiceNow AI Agent Studio
current
low code platformManaged ServiceNow platform
ServiceNow's managed authoring environment for configuring, testing, and deploying AI agents, tools, knowledge, triggers, and agentic workflows.
agent authoringtoolsknowledgetriggers
Snowflake
Snowflake Cortex Agents
current
managed platformManaged Snowflake platform
A fully managed Snowflake platform for agents that plan tasks, call tools, execute code, and reason across governed structured and unstructured data.
Explore how concepts connect—or trace how one search reached its answer.
The visual layer draws exact kernel, CATS, and Tier-S evidence. It cannot change ranking, invent a term, widen a relationship path, or alter a receipt.
RendererPreparing WebGPU
Trace a real questionThe current glossary search updates this view automatically.
Quantum fieldWaiting
Path coherence0%
RendererOpen the trace to prepare WebGPUWaiting for a routed search
TIER-S · NEGATIVE PATHS · FAIL-CLOSED RETRIEVAL
A trustworthy search system must prove how it stops.
These tests deliberately challenge exact identity, typed filters, lifecycle evidence, semantic support, Wasm availability, cache identity, and replay. Tier-S may admit the selected path, fall back to an exact executor, ask for clarification, or abstain. It may never let an optimized path approve itself.
A proposed ranking tries to move an exact acronym below a looser candidate.
Expected safe behaviorReject the ordering and restore the exact canonical term.
02Typed filter bypassNot run
A candidate outside the selected lifecycle filter is injected into the result set.
Expected safe behaviorQuarantine it; every admitted result still satisfies the filter.
03Stale lifecycle claimNot run
A current product is returned with tampered maintenance metadata.
Expected safe behaviorReject the altered claim and restore canonical lifecycle and sources.
04Unsupported semantic proposalNot run
A low-support concept is proposed for an unknown question.
Expected safe behaviorRefuse to manufacture an answer and safely abstain.
05Semantic path unavailableNot run
The proposal-only semantic executor is unavailable for a natural-language question.
Expected safe behaviorWithhold T6 and continue through deterministic retrieval.
06Wasm kernel unavailableNot run
The accelerated C17/WebAssembly executor is unavailable.
Expected safe behaviorKeep exact JavaScript search available without losing authority.
07Cache identity mismatchNot run
A cached result no longer matches the active corpus or configuration identity.
Expected safe behaviorReject the cache and recompute a fresh deterministic result.
08Replay divergenceNot run
A replayed result hash is deliberately perturbed after a sealed search.
Expected safe behaviorExpose the mismatch instead of claiming a successful audit.
OVERLIFT METHOD · CATS · TIER-S · EXACT FALLBACK
Inspect which path CATS selected—and what Tier-S actually admitted.
CATS classifies the request and chooses the least expensive qualified executor. Tier-S then shadow-checks exact identity, filters, lifecycle, official sources, support, cache identity, and replay before anything is admitted. T6 semantic envelopes and T7 explanations remain proposal-only.
Exact JavaScript search and DOM browsing remain available if Wasm, semantic proposals, cache admission, or GPU initialization fails. Unsupported results can abstain.
1Frame
Normalize the request, recover bounded spelling, classify intent, and bind filters.
2Qualify
Determine which T0–T7 executors are eligible without forcing every query through every tier.
3Select
CATS chooses the least expensive qualified safe path and records why alternatives were withheld.
4Execute
Run exact, lexical, graph, Wasm, cached, semantic, or explanatory retrieval inside its sandbox, capability, and authority boundary.
Seal the selected path, admitted path, Tier-S verdict, results, fallback state, and replay identity in one receipt.
LEARN THE SECURITY BOUNDARY
Retrieval can inform an agent. Retrieved content cannot grant it authority.
The glossary demonstrates the same OverLift rule it teaches: exact and semantic retrieval may propose useful evidence, but source eligibility, permissions, capability admission, deterministic fallback, Tier-S verification, receipts, and replay decide what the product may trust.