Reference knowledge
Understand AI agents from first principles.
Definitions, architectures, security, memory, protocols, multi-agent systems and governance — written as an evidence-first reference rather than a marketing glossary.
AGENTENCODE RESEARCH
Multi-Agent Intelligence: Research & Architecture
How collaborating AI agents work: orchestration, delegation, external benchmarks and auditable evidence.
Explore the research guide → · Orchestration patterns · Studies & benchmarks · Evidence methodology
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Artificial Symbiotic Intelligence: from agents to agent systems
Artificial Symbiotic Intelligence is a systems perspective in which useful intelligence emerges from interacting agents, tools, rules, institutions and oversight rather than from one model acting alone.
What is an AI agent?
An AI agent combines a model with goals, context, tools and an execution loop so it can work through multiple steps and act on external systems.
AI agent vs. chatbot: what is the difference?
A chatbot is primarily conversation-oriented; an AI agent can pursue a goal across multiple steps, use tools and potentially change external systems within defined permissions.
Agentic AI: what does “agentic” mean?
Agentic AI describes systems that can pursue goals through multiple steps, use tools, react to results and exercise bounded autonomy instead of only generating a single response.
What is a research agent?
A research agent searches, reads, compares and synthesizes information across multiple steps. Its quality depends on source selection, traceability and how well it separates evidence from interpretation.
MCP explained: Model Context Protocol for AI agents
Model Context Protocol (MCP) standardizes how AI applications connect to tools, resources and external context. It improves interoperability, but it is not a security mechanism by itself.
A2A explained: how AI agents work together
Agent2Agent (A2A) is a protocol for agent discovery and task-oriented communication between compatible agents. It addresses agent-to-agent coordination rather than tool access.
How do AI agents work?
AI agents typically operate in a loop: understand the goal, collect context, plan a next step, use a tool, observe the result, update state and decide whether to continue.
Types of AI agents: from research to enterprise automation
AI agents are more useful to classify by task, autonomy and execution environment than by marketing names. Common categories include research, coding, workflow, service, sales, data and enterprise agents.
Multi-agent systems explained
A multi-agent system coordinates several specialized agents or roles so they can divide work, exchange results or supervise one another. The benefit is specialization; the cost is more coordination and more trust relationships.
Autonomous AI agents: what autonomy really means
Autonomy is not an on/off property. AI agents can range from read-only analysis to proposing actions, acting with approval, or executing bounded tasks automatically.
AI agent security: risks, controls and checklist
AI agents are security-relevant because they can combine untrusted inputs with credentials, tools and autonomous action. Safe deployment requires least privilege, input isolation, human control, identity and auditability.
Tool use in AI agents: why tools matter
Tool use lets an AI agent do more than generate text: it can search, run code, access data or change external systems. That capability is powerful because it creates a real action surface.
ChatGPT dots: how an always-on agent changes the model
ChatGPT dots is an always-on agent concept built around persistent work, scheduled tasks, cloud execution, connections and rules. Its significance is the combination of continuity and action, not just conversational memory.
Memory in AI agents: what gets remembered and why it matters
Agent memory can preserve working state, conversation history, learned preferences or retrieved facts across steps and sessions. Different memory types have different privacy, security and reliability implications.
Human-in-the-loop for AI agents: approvals and control
Human-in-the-loop (HITL) means that an agent pauses, escalates or asks for human judgment at defined points instead of executing every decision autonomously.
AI agents for individuals: useful everyday applications
For individuals, AI agents are most useful when they reduce repetitive research, planning or digital work without requiring broad access to sensitive accounts.
AI agents for enterprises: deployment, governance and selection
Enterprise AI-agent programs work best when they begin with a measurable use case, narrowly scoped data and permissions, explicit human controls and operational observability.
How many good AI agents are there in 2026?
There is no defensible single number for “good AI agents” in 2026 because the count depends on what qualifies as an agent, which products are active and what evidence standard is used.
How to use this library
Concept first, evidence second, deployment decision last.
1. Understand the concept
Use these references to distinguish agents from chatbots, understand tool use, memory, autonomy and multi-agent coordination.
2. Inspect a real product
Move to an agent profile to see current primary sources, trust signals and verified history.
3. Check governance
Use Trust & EU and Agent Check to turn broad governance questions into explicit evidence controls.
4. Verify before deployment
Scope, plan, region and configuration matter. Public evidence is a starting point for due diligence, not a substitute for deployment-specific review.