We can decisively say that AI in 2025 has shifted from mere conversation and search to the heart of action and workflow. AI-powered IDEs, autonomous AI agents, and more, you name it, and there’s an AI-powered solution for that. The recent ambitious projects driving this change are OpenAI’s ChatGPT Agent and Perplexity’s Comet. Both systems enhance user interaction significantly with agentic capabilities. AI will now not only answer questions but also autonomously complete tasks. That said, ChatGPT Agent and Comet differ dramatically in their design philosophy, architectures, and real-world applications. This ChatGPT Agent vs Perplexity Comet blog covers everything you need to know about them and how they compare to one another.
Let’s get started.
What Is Agentic AI?
“Agentic AI” refers to AI endowed not just with reasoning or information-retrieval skills, but with automated action-taking: the ability to chain multi-step decisions, take consequences into account, and effect change in the digital world autonomously. For example, an AI agent might plan and book a trip, triage emails, manage reports, or even initiate financial transactions, all from a single instruction. Of course, it can get a lot more complicated than this based on what we’re talking about, but what’s described above serves as a decent base.
OpenAI’s ChatGPT Agent: The Unified Research and Action Platform
ChatGPT Agent, as described in OpenAI’s 2025 system card, is built atop the experience of three OpenAI product lines:
- Deep Research – Multi-step, reference-backed research and report generation
- Operator – Visual browser automation and web interaction
- Terminal Tool – Sandboxed code execution with only limited network access
These forces combine into a single “agent” entity capable of:
- Automated browsing and form-filling
- Code execution and data analysis
- Slide, spreadsheet, and report generation
- Connecting to external data sources (Google Drive, APIs)
- Maintaining context across a conversation
- Multistep, tool-based workflows
Agentic tasks are supervised by a unified interface in ChatGPT and governed by both immediate user input and a sophisticated confirmation system: every action deemed consequential (e.g., sending an email, finalizing a purchase) triggers a confirmation before execution, ensuring the user remains in ultimate control.
How OpenAI’s Agent Thinks and Acts
The key innovation with ChatGPT Agent isn’t just tool access but intelligent orchestration:
- Dynamic tool selection: Agent uses different tools (browser, terminal, spreadsheet, code, connectors) based on context.
- Chain-of-thought planning: Tasks are decomposed into sub-steps and executed in series or parallel.
- Action narration: The agent describes its reasoning, current action, and lets the user observe or interrupt every step.
Safety, Security, and Trust
OpenAI’s central design principle is preventing harm before it occurs—especially as agents gain access to more user data and online actions. Key mechanisms include:
- Safety Training: Specialized instruction to avoid harms from content generation, privacy breaches, or interacting with dangerous or sensitive web content.
- Automated Monitors and Filters: System-level fail-safes that can block inputs, outputs, and actions based on ongoing risk analysis or emerging attack patterns.
- User Confirmations: Before sending emails, transacting, or editing in sensitive contexts, the system demands explicit user permission.
- “Watch Mode”: In contexts requiring elevated sensitivity (e.g., banking, health records), the Agent pauses if the user stops supervising the process.
- Terminal Network Restrictions & Memory Isolation: Code execution is sandboxed; agent memory is disabled to prevent data exfiltration across sessions.
OpenAI’s preparedness framework even applies biological and cybersecurity risks to its agents, activating “high capability” safeguards if a model could theoretically assist with severe risks, even if practical evidence is lacking.
Besides, OpenAI offers the most transparent set of benchmarks published to date for its Agent:
- Content Policy Compliance: Nearly perfect refusal rates (99%+) for disallowed content and privacy risks.
- Robustness to Jailbreaks: Cutting-edge resilience to adversarial prompts or “prompt injections.”
- Hallucination Rates: Slightly higher than recent GPT models on some factual QA sets, due in part to seeking multiple sources and surfacing ambiguities.
- User Confirmation Recall: 91%+ recall for requested confirmations before risky actions; 100% on the most critical actions (financial, editing permissions).
- Bias and Fairness: Net-bias metrics (gender, race, etc.) among the lowest ever measured, enhanced by stricter grading.
- Expert Evaluations: Independently tested for chemistry, biology, and cybersecurity risk, with an emphasis on never enabling “novice uplift” for high-stakes dangers.
ChatGPT Agent Limitations
OpenAI’s Agent is tightly coupled to the ChatGPT ecosystem, meaning:
- Customization is limited to OpenAI-sanctioned APIs and connectors
- Workflow logic, though powerful, is constrained to available tools and chained prompt flows
- Some users report over-cautious refusals, prioritizing safety over flexibility (e.g., not answering disambiguated questions even when policy allows).
Perplexity Comet: The Agentic Browser Reimagined
Perplexity designed Comet from first principles as not just an assistant, but an AI-first browser. The hypothesis: if agency is all about context, why not put the agent where context originates—inside the browser?
At its foundation, Comet combines:
- Chromium Base: A full-featured browser—tabs, navigation, extensions, and bookmarks—providing compatibility for almost any workflow.
- Sidebar-based Comet Assistant: An integrated agent that sees everything you see. It can summarize, recommend, shop, fill forms, or multitask directly within any site or set of tabs.
- Real-Time Web Search: Perplexity’s answer engine cross-indexes the web in real time, integrating recent data and citations from across sources, including its proprietary model Sonar and external LLMs (GPT-4.5, Claude 4.0 Sonnet, etc.).
- Seamless Task Automation: Execute actions like scheduling, shopping, briefings, or knowledge extraction without ever leaving the browser, and without pasting information between windows.
- Personalized Memory: Comet learns user workflows, preferences, and frequently performed actions, organizing research and tasks proactively.
Architecture for Extensibility and Developer Control
Comet stands out for its modular, chain-of-thought architecture:
- Prompt Chains: Each notebook-like “cell” performs a function—API call, web search, code execution, summary, etc.—and can be chained together for composable, multi-step workflows.
- Native API Support: Developers can integrate custom APIs and SQL data sources directly. Unlike OpenAI’s ecosystem, where tool access is limited to what OpenAI allows, Comet is geared for extensibility.
- Universal Context Window: The AI agent has continuous access to all open tabs, live data, and user actions, enabling contextual reasoning beyond traditional chat interfaces.
Perplexity Comet Use Cases in Practice
Comet redefines what users can delegate to AI. Common examples include:
- Chart Analysis: Identify support/resistance zones on trading charts, with real-time annotation and direct execution in trading platforms or spreadsheets.
- Automated Email Unsubscription: Use natural language (“Unsubscribe me from any emails I ignored for three months”) and Comet acts across webmail autonomously.
- Travel Planning: From instruction (“Plan my Barcelona trip, €1,000 budget”) to final itinerary, all steps—search, compare, book—are handled inside the browser.
- Research Synthesis: Open a dozen tabs on a topic, and Comet will synthesize key points, generate decision summaries, and suggest next steps—cross-referencing live web data.
- In-tab Multi-modal Operation: Summarize YouTube tutorials while watching, link supporting docs, and even act on other tabs while video plays.
Perplexity Comet Safety and User Control
Perplexity’s approach to safety and user oversight is practical and evolving:
- Explicit Permissions: Users can grant or restrict access to sensitive workflows, such as shopping, form submissions, or email automation.
- Built-in Audit Trails: All agent actions can be reviewed in real time, and users can pause or revise automated suggestions before accepting.
- Context-Awareness and Learning: By maintaining a local model of a user’s active tasks, Comet can avoid repetitive automation or accidental data exposures—though edge cases (like unexpected email sends) have been reported.
- Privacy Positioning: As context and orchestration occur locally within the browser, minimal personal data leaves the user’s machine, compared to cloud-dependent agent frameworks.
Perplexity Comet Limitations
- Subscription Gating: As of July 2025, Comet is limited to Perplexity Max subscribers ($200/month) or early invitees. Wider access is promised but not yet delivered.
- Potential for Over-Automation: Handling multi-step actions in context-rich tabs can lead to unexpected outputs if instructions are ambiguous or if the system makes assumptions based on erroneous context.
- New Paradigm Steep Learning Curve: For many users, the fusion of browser and agent may initially feel less transparent, as automation blends with ordinary browsing, raising the stakes for missteps or neglected oversight.
ChatGPT Agent vs Perplexity Comet: Comparative Analysis
To summarize: ChatGPT’s agent is integrated within its app, using a sandboxed browser for context, with extensibility primarily through OpenAI-bound APIs. In contrast, Perplexity Comet operates as a standalone browser, offering persistent, unified context across all tabs and extensive extensibility via API, SQL, and plugin support. While ChatGPT uses stepwise, confirmation-based workflows, Comet employs fluid, event-driven routines, catering to different user needs from internal task automation to power-user research and development.
ChatGPT Agent vs Perplexity Comet: Use-cases & Scenarios
OpenAI ChatGPT Agent
- Creating high-quality research reports with integrated data analysis, visualizations, and structured slides
- Automating customer support or helpdesk triage with strict workflow gating
- Executing sandboxed code or managing cloud-stored datasets securely
- Acting on “closed world” tasks within a corporate ecosystem where compliance, audit, and confirmations are essential
Perplexity Comet
- Summarizing and prioritizing the content of 10 or more browser tabs in one action
- Managing end-to-end ecommerce routines: comparing, buying, tracking, and summarizing receipts, all within browser
- Implementing developer-driven workflows with API, SQL, or multi-app chaining
- Research requiring synthesis across real-time sources, up-to-the-minute news, and cross-platform web context
- Handling mixed media and multi-modal data (video, charts, web forms) with screen-driven actions
Limitations and Ongoing Challenges
Both products remain works in progress, and neither is a true “digital generalist” yet.
- OpenAI’s Agent must integrate external tool ecosystems more flexibly and reduce over-refusals that frustrate power users.
- Perplexity Comet needs wider access, improved error handling, and more robust safeguards for “side effects”—actions the agent takes without explicit, granular user input.
Additionally, as both systems learn from ongoing deployment, the risks of prompt injection, data leakage, and runaway automation remain top priorities for iteration and innovation.
The Bottom Line
OpenAI’s ChatGPT Agent and Perplexity’s Comet offer two different approaches to AI assistance. OpenAI provides a safe and controlled agent that focuses on reliability and compliance, good for users and teams who want to manage risks. Perplexity Comet, on the other hand, utilizes the internet to help users work more efficiently and access a wider range of information, making it easier to delegate tasks.
Choosing between them isn’t about which one is better; it’s about which approach—keeping control or empowering users to delegate—fits your needs, how much risk you want to take, and the workflows you want to improve. The growth of AI agents is not just a technical development; it’s a major change in how people work with digital tools and the information that drives their curiosity and tasks.