Key takeaways
- AI agents use a model to choose actions and call tools in a loop until a goal is met.
- Build agents with clear tools, step limits, guardrails, evaluation and human approval for risky actions.
- Frameworks such as LangGraph, CrewAI and the OpenAI and Claude agent SDKs handle the plumbing.
- Language model agents emerged in 2022 and 2023 and reached mainstream products in 2024 and 2025.
- Agents should respect site terms, prefer APIs and hand CAPTCHAs to humans.
An AI agent is a system where a language model decides which actions to take, calls tools such as APIs, browsers or code, observes the results and repeats until a goal is met. Agents moved from experiments to products between 2023 and 2026. I lead engineering at SCULT INDIA and shipped five AI apps in 2026, including RAG.NextUpgrad and DocuLens AI, so this page covers what agents are used for, how they are built, frameworks, differences, history, apps, Zapier, scraping, CAPTCHAs and earning.
What are AI agents used for?
AI agents handle multi-step tasks that need decisions and tools: writing and fixing code, answering support tickets with account actions, researching topics across sources, analysing data, processing documents, and automating workflows across business apps.
Coding agents such as Claude Code and Codex read repositories, edit files and run tests. Support agents look up orders and issue refunds within policy limits. Research agents search the web, read pages and write cited summaries. Operations agents triage alerts, query logs and suggest fixes. Back-office agents extract data from invoices and update systems, a pattern close to what DocuLens AI does with schema validation. The best use cases have clear goals, accessible tools, measurable success and tolerable error costs, with humans approving risky actions.
How are AI agents developed?
Developers define the task and success criteria, choose a model, expose tools as functions with clear schemas, and run a loop where the model plans, calls tools, reads results and continues until done or stopped.
Also asked as: how ai agents are developed
Start with the simplest design, often a fixed workflow, and add autonomy only where needed. Write precise tool descriptions, return concise tool results, and set limits on steps, time and spending. Add guardrails such as permission checks, input validation and human approval for irreversible actions. Store state and memory outside the model when tasks span sessions. Build an evaluation set of realistic tasks and measure success rates before launch, then log every step for debugging. The Model Context Protocol lets agents connect to tools and data through a standard interface.
What is an AI agents framework?
An AI agents framework is a library that handles the plumbing of agent systems: model calls, tool definitions, the reasoning loop, memory, multi-agent coordination, streaming, tracing and error handling, so developers focus on tasks and tools.
Also asked as: what is ai agents framework
Popular options include LangGraph for graph-based stateful agents, CrewAI for role-based teams, Microsoft's AutoGen, the OpenAI Agents SDK, the Claude Agent SDK, LlamaIndex for data-heavy agents, Google's Agent Development Kit, and Mastra for TypeScript. Frameworks speed up prototyping and add observability, but they also add abstraction that can hide prompts and make debugging harder. For simple agents, a direct loop with a provider SDK is often clearer. Choose based on language, deployment needs, control over prompts and how easily you can test and trace runs.
How are AI agents different from chatbots and automation?
A chatbot responds to messages, mainly producing text. Traditional automation follows fixed rules you define. An AI agent decides its own steps toward a goal, choosing tools, acting on systems and adapting to results along the way.
For example, a chatbot can explain how to reset a password, a rule-based automation sends a reset link when a form is submitted, and an agent can investigate why a login fails, check account status, reset credentials and confirm with the user. The model itself is the reasoning engine, while the agent is the system around it: tools, memory, loop and guardrails. Agents handle messy, variable tasks better but are less predictable, slower and costlier than fixed automation, so many products combine both approaches.
When did AI agents come out?
The idea of software agents dates back decades in AI research, but language model agents emerged in 2022 and 2023, after the ReAct paper showed models interleaving reasoning with actions and projects such as AutoGPT went viral.
Function calling arrived in major APIs in 2023, making tool use reliable for developers. In 2024, Anthropic introduced computer use for Claude and launched the Model Context Protocol to connect agents with tools and data. In 2025, browser agents such as OpenAI's Operator and coding agents such as Claude Code and Codex reached many users, and businesses began deploying agents in support, sales and operations. Capabilities keep improving quickly, but reliability, security and cost remain the main constraints on wider adoption.
Will AI agents replace apps?
Not entirely. Agents will increasingly sit between people and apps, completing tasks through APIs and interfaces so users need not navigate each app, but the underlying services, data and specialised interfaces will still exist.
Many simple tasks, such as booking, reordering or filing forms, may shift to conversational agents, which pushes companies to expose APIs and agent-friendly interfaces, including MCP servers. Apps that rely on rich visual interaction, creative control, real-time collaboration or trust, such as banking and design tools, will remain important, though they will embed agents inside. Businesses that make their services easy for agents to use may win users, while apps that depend only on screen time may lose relevance. Expect coexistence rather than replacement.
Can Zapier create AI agents?
Yes. Zapier Agents lets you build AI agents in plain language that act across the thousands of apps Zapier connects, such as Gmail, Slack, HubSpot and Google Sheets, using your data and instructions.
You describe the agent's job, connect the apps and data sources it may use, and set triggers such as new emails or form submissions. Agents can research leads, draft replies, update records and summarise information. Zapier also offers an MCP server so agents built elsewhere can use Zapier's app actions as tools. This no-code approach suits business teams automating repetitive work. Review agent actions regularly, limit permissions to what each agent needs, and require approval before sending external messages or changing important records.
Can AI agents scrape websites?
Technically yes. Agents can fetch pages, run headless browsers and extract data, and many agent frameworks include web tools. Whether they should depends on each site's terms of service, robots.txt rules, copyright and privacy laws.
Prefer official APIs, data exports or licensed datasets where available, since they are more reliable and clearly permitted. When scraping is allowed, respect robots.txt, rate limits and identification headers, avoid collecting personal data without a legal basis, and do not republish copyrighted content. Agents are also costly scrapers, since each page passes through a model, so traditional scrapers suit large volumes, with agents handling messy pages or interpretation. Log sources and timestamps so you can cite and refresh data later.
Can AI agents bypass CAPTCHA?
They should not. CAPTCHAs exist to block automated access, and deliberately bypassing them usually violates the site's terms. Reputable agents, such as browser agents from major AI companies, are designed to stop and ask the user to complete CAPTCHAs.
Some models can technically read simple image CAPTCHAs, and dedicated solving services exist, but using them for automation risks account bans, legal problems and harm to sites that rely on bot protection. If your agent repeatedly meets CAPTCHAs, the site is signalling that it does not want automated access, so look for an official API or partnership. For your own sites, modern bot protection relies on behavioural signals rather than puzzles alone. Design agents to hand control to a human whenever a site asks for verification.
How can you earn with AI agents?
Build agents that solve specific, costly business problems, such as qualifying leads, answering support queries, processing documents or preparing reports, then sell them as services, subscriptions or custom builds to businesses that feel that pain.
Also asked as: how to earn with ai agents
Freelancers and agencies earn by designing and maintaining agent workflows with tools such as Zapier, n8n or custom code. Developers build niche software products around one agent use case, such as invoice extraction for accountants. Others sell templates, courses or consulting. Success depends on reliability, so measure accuracy, handle edge cases, add human review and show clear time savings. Avoid promises of passive income; agents need monitoring, updates and support. Start with one paying customer and a narrow problem before scaling.
Sources
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.
