What is AI Agent Chat?
AI Agent Chat is a conversational interface where you describe automation tasks in plain English and an AI agent carries them out for you. Instead of dragging nodes onto a canvas, writing scripts, or configuring complex rules, you simply type what you need — and the agent figures out the rest.
Traditional automation tools fall into two camps: visual builders that require you to manually map every step, and code-based frameworks that demand programming skills. AI Agent Chat eliminates both barriers. You communicate your intent the same way you'd explain a task to a colleague, and the agent translates that into a sequence of real browser actions, data extractions, and integrations.
Whether you need to scrape product data, fill out web forms, or push results to Google Sheets, the conversation starts the same way: describe what you want.
Who Is It For?
AI Agent Chat is designed for operations teams, marketers, analysts, and founders who need to automate repetitive web tasks but don't want to invest hours learning a new tool. If you can describe the task, you can automate it.
How It Works
Getting started is straightforward:
- Type a prompt — for example, *"Find the top 100 Y Combinator companies from the current batch and save their name, website, and description to Google Sheets."*
- The agent opens a real browser — not a simulated one. It navigates to the target site, waits for dynamic content to load, and interacts with elements exactly as you would. You can watch every action through the live browser view.
- Mid-session guidance — if the agent takes a wrong turn or you want to refine the task, send a message. The agent reads your correction and adjusts immediately. This makes complex, ambiguous tasks manageable without upfront planning.
- Results are delivered — extracted data lands in your chosen format: Excel files, CSV, Google Sheets, Notion databases, or any other connected tool.
The entire interaction feels like chatting with a capable assistant who happens to be very fast at browser tasks.
What Can You Automate?
AI Agent Chat handles a wide range of tasks across industries:
Data collection — scrape websites, monitor prices, track competitor product catalogs, aggregate job listings. Pair with Data Extraction for structured output.
Lead generation — find contact information on company websites, enrich leads with LinkedIn data, push results to your CRM or Airtable.
Content workflows — gather research from multiple sources, summarize articles, compile reports. Use AI & Content tools for summarization and transformation.
Form submissions — automate repetitive data entry on government portals, vendor systems, or internal tools.
Monitoring and alerts — check for stock availability, regulatory updates, or competitor changes on a schedule.
Browse the templates library for ready-made starting points across these categories.
From Chat to Scheduled Workflow
A single chat session solves an immediate need. But what about tasks you need to run daily, weekly, or on a trigger?
Every completed AI Agent Chat session can be converted into a reusable workflow with one click. The system analyzes your session, extracts the successful steps, and builds a visual workflow in the Visual Workflow Builder. From there you can:
Schedule runs — daily price checks, weekly lead generation, monthly report compilation
Add logic — conditional branches, loops, error handling via Logic & Flow
Parameterize inputs — swap out URLs, search queries, or date ranges without rebuilding
Connect integrations — deliver results to Slack, Google Sheets, Notion, or 200+ other tools
This means your one-off chat becomes a production-grade automation pipeline, no rebuilding required.
Built-In Intelligence
The AI agent improves over time through Cross-Session Learning. Every successful interaction teaches the system:
Which selectors work on specific websites — so future sessions on the same site start faster
Which navigation paths succeed — reducing trial-and-error on familiar sites
Common pitfalls on certain domains — like login walls, CAPTCHAs, or unusual page structures
You don't need to configure any of this. The learning happens automatically in the background, and every user on your account benefits from accumulated knowledge.
Budget Management
Each session consumes a predictable amount of resources. You can see token usage in real-time, receive warnings at 70% and 90% budget thresholds, and the agent prioritizes completing its current objective efficiently. Check the pricing page for details on session budgets and plan limits.
Getting Started
The fastest way to try AI Agent Chat is to sign up for a free account and type your first prompt. No credit card required for the trial. If you want inspiration, browse the templates library or check the glossary for terminology.
Best Practices
Getting the most out of AI Agent Chat comes down to how you communicate with the agent and how you structure your requests:
Be specific about your output format and fields. Instead of "scrape this website," say "extract the company name, website URL, founding date, and number of employees from each company listed on this page, and save the results to Google Sheets." The more specific your prompt, the better the agent's first attempt will be. Specific prompts also lead to better Cross-Session Learning patterns that transfer to future sessions.
Use mid-session guidance to refine, not restart. When the agent takes a wrong turn, send a correction message rather than starting a new session. The agent learns from corrections within a session — "skip that section and focus on the product listing table below the fold" is more effective than canceling and reprompting. Mid-session guidance also costs fewer tokens than a fresh start.
Start with a small sample before going to scale. Ask the agent to extract data from the first 5 results before committing to all 500. Review the sample output, confirm the fields are correct, and then tell the agent to proceed with the full dataset. This preview-then-scale approach prevents wasting budget on a misunderstood task.
Convert successful sessions to workflows immediately. Once a chat session produces the results you want, click "Convert to Workflow" before closing. The conversion captures the agent's successful approach as a reproducible pipeline. If you close the session without converting, you can still recreate the workflow, but you lose the automatic wiring of data connections between steps.
Combine chat sessions with [scheduled workflows](/features/scheduled-execution) for recurring tasks. Chat is perfect for exploration and one-off tasks. For anything you need to run more than once, convert to a workflow and schedule it. This frees you from manually initiating the same task repeatedly and ensures consistent results. Learn more about the transition from manual to automated workflows in our AI workflow automation guide.
Security & Compliance
AI Agent Chat sessions run in the same isolated execution environments as all Autonoly automations. Each session gets a fresh browser instance with no residual data from previous sessions. When a session completes, the execution environment is destroyed entirely. Login credentials entered during a chat session are stored in the encrypted credential vault — they are never exposed in the chat log, execution logs, or session recordings.
Chat conversations are stored in your workspace and are accessible to workspace members with appropriate permissions. For organizations with data retention requirements, chat history follows the same retention policies as all other workspace data. Sessions can be individually deleted, and account deletion permanently removes all associated chat history. The AI models powering the agent process your prompts in real-time but do not retain your conversation data for model training.
For teams handling sensitive data during chat sessions (such as logging into banking portals or healthcare systems), note that the live browser view shows the agent's activity in real-time but does not record or persist the video stream. Credentials visible on-screen during authentication are protected by the same isolation guarantees that apply to all Autonoly sessions. Learn more about Autonoly's security architecture on the Security feature page.
Common Use Cases
AI Agent Chat is the most versatile entry point for automation because it handles almost any web task without prior configuration. Here are detailed real-world examples:
Rapid Market Research
A startup founder types: "Go to Crunchbase, find the top 50 AI startups that raised Series A in the last 12 months, and save their name, website, funding amount, and investor list to Google Sheets." The agent navigates to Crunchbase, applies the appropriate filters, extracts the data from the search results, and delivers a clean spreadsheet — all within a single chat session lasting about 10 minutes. The founder then converts the session to a monthly workflow to track new entrants automatically. For a deeper understanding of how AI agents handle web research tasks, see our guide on AI agent web scraping.
Building Custom Data Pipelines Through Conversation
A data analyst needs to combine information from three sources: product reviews from Amazon, pricing data from a competitor's site, and inventory levels from a supplier portal. Rather than building three separate workflows, they describe the entire pipeline in a single chat session: "Scrape the top 20 reviews for product X on Amazon, then check the price for the same product on CompetitorSite.com, and finally log into our supplier portal to check current stock levels. Combine all three into a single Google Sheet with columns for review text, rating, competitor price, and stock quantity." The agent handles the multi-site navigation, login flows, and data merging in one go. Read more about building multi-source data pipelines in our web scraping best practices guide.
Non-Technical Team Empowerment
A marketing manager needs weekly social media engagement data compiled from three platforms. Previously, this required an analyst to manually visit each platform, export CSV files, and merge them in Excel. Now the manager types a natural-language prompt describing what they need, the agent collects the data across all platforms, and the results land in a shared Google Sheet. The manager converted this to a scheduled workflow that runs every Monday morning. No engineering resources needed, no ticket filed, no waiting. Our marketing automation guide covers more scenarios where non-technical teams can self-serve their data needs.
Exploratory Automation for Complex Sites
A compliance analyst needs to extract data from a government portal with a multi-step search flow, complex filters, and results that span dozens of paginated pages. Rather than trying to map this as a workflow upfront, they use AI Agent Chat to explore the portal interactively. The analyst provides guidance as the agent navigates — "click the advanced search tab," "set the date range to the last quarter," "extract all results including the filing date and document link." Once the session produces the right results, it is converted to a repeatable workflow with a single click.