Key takeaways
- Models are stateless, so agent memory comes from stored history, databases and checkpoints.
- Many agents connect to tools through MCP, though direct function calling also works.
- Agents run wherever their code runs, from cloud servers to your own laptop.
- Trust agents only for tested tasks, with limited permissions and human review.
- Agents create value on checkable, repetitive work, but marketing often overstates autonomy.
AI agents are software systems where models plan and act with tools, and practical questions about them come down to architecture and governance: where they run, what they remember, how they connect to tools and whether they can be trusted. I build AI products with guardrails, such as RAG.NextUpgrad, so this page covers persistence and statelessness, MCP, who builds agents, where they live, trust, hype, making money, sharing agents and what they look like.
Do AI agents persist?
Agents can persist information if their designers add memory. The underlying model forgets everything between requests, so persistence comes from stored conversation history, databases, memory files, checkpoints or vector stores that the agent reads and writes.
Frameworks such as LangGraph save state with checkpointers, so an agent can pause for human approval and resume later, or continue a conversation days afterwards. Assistants such as ChatGPT and Claude offer memory features that store user preferences across chats. Long-running agents may persist task plans and progress notes so they can recover after interruptions. Store memories in structured form so agents retrieve only what is relevant. Persistence raises privacy questions, so let users view and delete what agents remember.
Are AI agents stateless?
The language model inside an agent is stateless: each request contains everything it sees, and nothing is remembered afterwards. The agent system around it is usually stateful, tracking conversation history, tool results, plans and progress.
Designers choose how much state to keep. Stateless agent services are simpler to scale, since any server can handle any request when state lives in a database. Stateful designs keep sessions in memory for speed but complicate scaling and recovery. Managing state well is central to agent reliability: too little and agents forget their goals; too much and context fills with irrelevant history, raising costs and confusing the model.
Do AI agents use MCP?
Many do. The Model Context Protocol, introduced by Anthropic in 2024, has become a common way for agents to connect to tools and data, with support in Claude, ChatGPT, Gemini tools, Microsoft products, coding agents and many frameworks.
MCP servers expose tools, resources and prompts that any compatible agent can use, so one integration works across many AI applications. Agents can also use tools without MCP, through direct function calling defined in code. MCP helps most when you want reusable integrations, such as a company database or ticketing system, available to several assistants. Treat MCP servers as privileged access points: authenticate them, restrict permissions and log their use.
Who builds AI agents?
AI agents are built by AI labs such as OpenAI, Anthropic and Google, software companies adding agents to products, startups focused on specific tasks, enterprise developers automating internal workflows, and individuals using frameworks or no-code tools.
The core role is the AI engineer, who combines software engineering with LLM skills: designing tools, prompts, memory and evaluation. Product managers define goals and success measures, domain experts shape workflows and review outputs, and security teams set permissions. No-code platforms such as Copilot Studio, n8n and Zapier let non-developers build simpler agents. Students can build agents too, starting with a single tool and a clear, checkable task.
Where do AI agents live?
AI agents run wherever their code runs: on cloud servers, in containers or serverless functions, inside SaaS platforms, in desktop and mobile apps, in browsers, or on your own computer, as coding agents in a terminal do.
The model may run elsewhere, called through an API, while the agent's loop, tools and state live in your infrastructure. Local agents can use local models for privacy. Enterprise agents often run inside platforms such as Salesforce, ServiceNow or Microsoft 365, close to business data. State and memory usually live in databases, so agents can restart without losing progress. Where an agent runs affects latency, security, cost and what data it can access.
Can AI agents be trusted?
Trust AI agents for well-defined tasks where they have been tested, with limited permissions and human review for consequential actions. Do not trust them blindly: they can hallucinate, misread instructions and be manipulated by malicious content.
Trust should be earned through evidence: evaluation on realistic tasks, logs of every action, clear escalation to humans and gradual expansion of autonomy. Frameworks such as NIST's AI Risk Management Framework help organisations assess risks systematically. Users should know when they are interacting with an agent and how to reach a person. Design agents to decline or ask when uncertain, as RAG.NextUpgrad's confidence gate does, rather than guessing confidently.
Are AI agents overhyped?
Partly. Agents genuinely help with coding, research and support, but marketing often overstates their autonomy. Gartner predicted in 2025 that over 40 percent of agentic AI projects would be cancelled by the end of 2027, citing costs, unclear value or weak controls.
Hype shows up as demos that hide manual work, claims of replacing whole teams, and agents labelled autonomous that are really scripted workflows. Real value appears where tasks are repetitive, outcomes are checkable and humans supervise exceptions. Teams succeed by starting small, measuring results against a baseline and expanding only proven use cases. The technology is improving quickly, so today's limits will shift, but disciplined evaluation remains the best antidote to hype.
Can AI agents make money?
Agents can create business value by saving staff time, resolving support requests, qualifying leads or speeding up software development. Individuals can earn by building and maintaining agents for clients. But no agent reliably generates income on its own.
Common legitimate paths include automation consulting for small businesses, building specialised agents as products, and using coding agents to deliver freelance projects faster. Be sceptical of schemes promising passive income from autonomous agents, especially in trading or crypto, which carry real financial risk and are often scams. Value comes from solving specific problems customers will pay for, with reliable results and human oversight. Measure time saved or revenue gained before scaling any agent.
Can you share AI agents?
Yes, depending on the platform. Custom GPTs can be shared by link or published in OpenAI's GPT Store, Claude Projects can be shared within Team and Enterprise workspaces, Copilot Studio agents can be published to Teams, and code-based agents can be shared through GitHub.
Before sharing, check what the agent can access: shared agents may expose connected data, files or tool permissions to new users. Remove secrets from code and instructions, and use per-user authentication for connected services. Document what the agent does, its limits and how to report problems. Agent Skills and MCP servers also make agent capabilities shareable across tools. Version shared agents so changes do not surprise users.
What do AI agents look like?
AI agents have no physical appearance unless embodied in robots. Users see them as chat windows, sidebars inside apps, voice assistants, terminal tools such as Claude Code, or background processes that report results through notifications and logs.
Also asked as: how ai agents look like
Some agents visibly operate a browser or desktop, showing mouse movements and typing as they work. Many products give agents names, avatars or personalities, but these are design choices, not the agent itself. Behind the interface, an agent is code running a loop with a model, tools and memory. Embodied agents, such as warehouse robots or humanoid research robots, combine AI planning with physical sensors and motors. Clear interfaces that show what an agent is doing build user trust.
Sources
- LangGraph, Persistence conceptslangchain-ai.github.io
- Model Context Protocolmodelcontextprotocol.io
- Gartner, Over 40 percent of agentic AI projects will be canceled by end of 2027gartner.com
- OpenAI, Introducing GPTsopenai.com
- Anthropic, Building effective agentsanthropic.com
- NIST, AI Risk Management Frameworknist.gov
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
