Key takeaways
- AI agents use models to plan and act with tools, unlike chatbots that only answer.
- Agents date back decades, but language model agents emerged in 2022 and 2023.
- Agents are software, not people, and deployers remain accountable for their actions.
- Agents can email or trade through APIs, but need approvals, limits and regulatory compliance.
- Inventions using agents can be patented, but AI cannot be an inventor, and AI taxes remain debated.
AI agents are software systems that use AI, usually a language model, to pursue goals by planning and taking actions with tools. The idea is decades old, but language models made general-purpose agents practical from 2023. I build AI products with guardrails, such as RAG.NextUpgrad, so this page covers what AI agents mean, when they started, why we need them, how many exist, who sells them, whether they are people, email, stock trading, patents and taxes.
What does AI agents mean?
AI agents are programs that perceive information, decide what to do and act toward a goal with some autonomy. Modern AI agents use a language model to plan steps and call tools, such as search, email or databases, to complete tasks.
The word agent means something that acts on someone's behalf. A chatbot answers questions; an agent takes actions, such as booking a meeting, fixing code or processing a document, and checks results along the way. Agents range from simple single-tool assistants to multi-agent systems coordinating many specialised workers. Their usefulness depends on reliable tools, clear goals, permissions and human oversight, not just the intelligence of the underlying model.
When did AI agents start?
The idea of software agents dates to early AI research, and the intelligent agent became the central framing of the field's leading textbook by Russell and Norvig in 1995. Language model agents emerged in 2022 and 2023.
Earlier agents included expert systems, game-playing programs, reinforcement learning agents and 1990s software agents that filtered email or searched the web. The ReAct paper in 2022 showed language models could interleave reasoning and tool use, and open-source projects such as AutoGPT and BabyAGI popularised autonomous agents in early 2023. Since then, tool calling became a standard model feature, and agent products for coding, research and customer service reached mainstream use from 2024 onward.
Why do we need AI agents?
We need AI agents to handle multi-step work that wastes human time: gathering information, processing documents, routing requests, writing and testing code, and operating software, freeing people for judgement, creativity and relationships.
Chatbots answer, but people still copy results between tools and finish the work. Agents close that gap by acting across systems, such as reading a support ticket, checking the order database and drafting a resolution. They also make expertise more accessible, such as helping small businesses automate tasks without hiring specialists. The need is real but specific: agents add most value for repetitive, well-defined workflows with checkable outcomes, and less for ambiguous, high-stakes decisions.
How many AI agents exist?
No reliable count exists, because anyone can build an agent with an API key and a few lines of code. Very large numbers of custom assistants and agents have been created across consumer platforms, developer frameworks and enterprise tools.
Counts depend on definitions: a custom chatbot with one tool may or may not qualify. Major platforms, such as OpenAI's GPTs, Microsoft Copilot Studio, Salesforce Agentforce and open-source frameworks like LangGraph, each host many agents built by users and companies. What matters more than the total is how many agents run reliably in production with measurable results, which remains a much smaller, though fast-growing, number.
Who is selling AI agents?
Sellers include AI labs such as OpenAI, Anthropic and Google; enterprise software companies such as Microsoft, Salesforce, ServiceNow and SAP; cloud providers such as Amazon; automation firms such as UiPath; and many startups focused on specific tasks.
Startups sell agents for customer service, coding, sales outreach, recruiting, legal review and finance operations, often priced per task or outcome. Platforms let businesses build their own agents with templates and connectors. When buying, evaluate on real workflows: resolution rates, error handling, security, integrations and how humans stay in control. Beware of demos that hide manual work behind the scenes, and ask for references from customers using the product in production.
Are AI agents real people?
No. AI agents are software, not people, even when they write naturally or use human-sounding voices. Responsible deployments disclose that users are talking with AI, and laws such as the EU AI Act require such transparency in many situations.
Some businesses give agents names and personalities, which can confuse users into believing a human is responding. Clear disclosure builds trust and lets people request a human when needed. Agents also cannot hold legal responsibility; the organisation deploying them is accountable for their actions. If you are unsure whether you are talking to a person, ask directly, and avoid sharing sensitive information until you know who, or what, will handle it.
Can AI agents send emails?
Yes. With access to an email tool or API, such as Gmail or Microsoft Graph, agents can draft, send, reply to and organise emails. Many products limit agents to drafts by default, requiring human approval before sending.
Common uses include replying to routine customer questions, sending follow-ups, scheduling meetings and summarising inbox threads. Risks include sending wrong information, replying to phishing emails, leaking confidential data and violating anti-spam rules. Safeguards include approval steps for external emails, allowed recipient lists, templates, rate limits and logs of every sent message. Prompt injection hidden in incoming emails is a real threat, so agents should treat email content as data, never as instructions.
Can AI agents trade stocks?
Technically, yes. Agents can connect to broker APIs to analyse data and place orders, and algorithmic trading has used automated systems for decades. But trading is regulated and risky, and agents can make costly mistakes quickly.
Retail algorithmic trading usually requires a broker that supports API access, and regulators set rules on automated orders; in India, SEBI has introduced a framework for retail algorithmic trading through brokers. Language model agents can misread data, hallucinate facts or react to manipulated news. Any automated trading needs strict limits, testing on historical and paper trading data, monitoring and kill switches. This page explains capabilities, not investment advice; consult a licensed adviser for financial decisions.
Can you patent AI agents?
You can patent inventions that involve AI agents, such as a novel technical method, if they meet patentability requirements. But an AI agent cannot be named as an inventor: courts in the UK and United States have ruled inventors must be human.
The DABUS cases tested whether an AI system could be an inventor, and courts rejected it, including the UK Supreme Court in 2023. Software-related inventions face additional hurdles: in India, computer programmes per se are excluded, so applications must show a technical effect or contribution. Patent applications must also disclose how the invention works. Many AI teams rely on trade secrets, speed and open-source strategy instead. Consult a patent attorney before filing.
Should AI agents be taxed?
It is debated. Supporters of a robot or automation tax argue it could fund retraining and social support if AI displaces workers. Critics argue it would slow productivity gains and be hard to define, since agents are software, not employees.
No country currently taxes AI agents directly; companies pay ordinary corporate and indirect taxes on revenue and services, including digital services. Alternatives proposed include taxing capital more heavily, adjusting payroll taxes, or funding workforce programmes from general revenue. Bill Gates suggested taxing robots in 2017, which sparked wide discussion. As AI changes employment, governments will likely revisit how tax systems treat automation, but practical definitions and enforcement remain unresolved.
Sources
- Russell and Norvig, Artificial Intelligence: A Modern Approachaima.cs.berkeley.edu
- Yao et al., ReAct: Synergizing Reasoning and Acting in Language Modelsarxiv.org
- EU AI Act, Article 50 transparency obligationsartificialintelligenceact.eu
- UK Supreme Court, Thaler v Comptroller-General of Patentssupremecourt.uk
- SEBI, Securities and Exchange Board of Indiasebi.gov.in
- Salesforce, Agentforcesalesforce.com
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.
