Collect Video Testimonials That Build TrustBest AI Research Agents
By Venkatraman C
Last updated on Sep 28, 2026AI Research Agents are AI systems designed to handle complex research tasks with greater autonomy than a standard search engine or chatbot.
### Research Planning and Execution Gemini Deep Research uses an explicit planning stage before executing research. It breaks a complex query into smaller subtasks, presents the research plan for user refinement, and then determines which tasks can run in parallel or need sequential execution. During research, the system repeatedly evaluates gathered information and uses those findings to determine its next research action. ### Research Sources and Context Deep Research can browse up to hundreds of websites and use Google Search and web technologies throughout its research loop. Users can optionally extend the research context with Gmail, Google Drive, Google Chat, and uploaded files. The system uses a large context window together with retrieval techniques to maintain continuity during a research session and support follow up questions. ### Long Running Research Deep Research is designed for tasks involving many model calls over several minutes. Google describes an asynchronous task management system that maintains shared state during execution and supports recovery from individual failures without restarting the entire research task. Users can leave Gemini after starting research and return later when the completed research is available.
### Research and Agent Execution Genspark Super Agent combines research with autonomous execution. It can break a research request into steps, choose tools, browse live information, work with files and connected data, cite sources, and produce finished outputs. Deep Research can also be delegated to a specialized background agent, allowing a research task to continue independently while other work proceeds. ### Execution Environment and Parallel Work The current Super Agent uses a dedicated sandbox with a real browser, filesystem, and execution capabilities. It is designed for longer multi step work and can continue running after the user leaves the session. Multiple tasks can also operate in parallel within a project, and tasks can build on results generated by other work in that project. ### Reusable Workflows and Context Super Agent can turn a completed workflow into a reusable Skill when instructed by the user. Saved Skills can be invoked in later conversations and shared with team members. The agent can also use files, connected accounts, Google Drive, AI Drive, and SecondBrain as context while carrying out research and other workflows.
### Research and Agent Orchestration Perplexity Computer combines research with agent execution rather than limiting the workflow to question answering. It can divide complex assignments into smaller tasks, deploy subagents, browse sources, extract information, and synthesize findings. This structure is useful for research workflows involving multiple sources, repeated investigation steps, and structured outputs. ### Workflow Automation and Deliverables Computer can combine web research with browser actions, connected applications, and recurring task execution. Research can continue into deliverable creation, including reports, spreadsheets, presentations, dashboards, websites, and applications. Recurring tasks also allow selected workflows to run autonomously over time rather than requiring a new manual prompt for each execution. ### Memory and Connected Context Brain in Research Preview provides continuing context for Max and Enterprise Max users. It builds a working model from projects, files, connected tools, previous sessions, and user corrections. Computer can use this context when handling requests that depend on earlier work, while individual Brain entries remain linked to their source information.
### Autonomous Agent Execution Manus separates lightweight Chat Mode from Agent Mode for complex work. In Agent Mode, the system autonomously plans and completes tasks based on user instructions rather than limiting interaction to conversational answers. Documented outputs include websites, slides, videos, and mobile applications, giving the agent an execution role across several types of digital work. ### Connected Tools and Workflow Actions Manus Connectors provide access to external applications, APIs, databases, and MCP servers directly within conversations. Documented examples include Gmail, Google Calendar, GitHub, Linear, monday.com, Supabase, and Postgres. Users can also create custom API or MCP connectors, allowing the agent to retrieve external information and perform actions in connected systems. ### API and Automation The Manus API exposes agent functionality through a REST interface. Developers can create and manage tasks, continue multi turn interactions, attach files, organize work through Projects, receive webhook notifications, use Skills, and configure custom agents. Manus can also participate in external workflows through Slack and Zapier, providing multiple ways to trigger and monitor agent execution.
