SciSpace Research Agent for Smarter Research | Step-by-Step Guide | SciSpace Webinar

SciSpace’s research agent is built to stitch together the scattered, multi-tool workflows researchers use today—turning literature review, peer review, manuscript/grant writing, data analysis, and even website creation into one guided, multi-step process with transparent intermediate outputs. The core pitch is that generic “agent modes” tend to be broad and unreliable for scholarly work, while this agent is tailored for research tasks, with research-specific workflows, tool access, and a context-aware plan that can run for long stretches without constant supervision.

The product starts from a practical pain point: researchers already assemble pipelines across SciSpace tools—copying outputs from literature review into other systems, pasting into different tools to generate charts, then moving again into word processors, spreadsheets, and slide decks. That manual hopping is time-consuming and hard to standardize. The agent is designed to collapse those steps into a single place, so users can communicate freely with one system that understands what they started with, what it produced, and what remains.

Under the hood, the agent turns a detailed user prompt into a multi-step plan, then executes it iteratively using “100+ tools and databases” plus built-in research workflows such as systematic literature review, peer review, and converting papers to code. It can also pause to ask the user for clarification when needed. A key UX feature is transparency: the interface shows a to-do checklist, live activity for the current step, and a growing list of files the agent generates on the fly—often including downloaded PDFs, intermediate CSVs, and final deliverables.

The webinar demonstrates several use cases. In a grant-writing example, the agent searches for relevant funding opportunities (including government grants), selects a top match, pulls supporting research via academic and web sources, and drafts a proposal in LaTeX—then self-corrects when LaTeX compilation fails by installing missing packages and simplifying the document to get to a working PDF. In a peer review workflow, the agent parses an uploaded ML-focused PDF, searches for related work on SciSpace, produces novelty/impact analysis, generates three distinct reviews from different perspectives (technical, application impact, and critical), and then compiles a meta review with prioritized issues.

For systematic literature review, the agent follows a Prisma-style protocol: defining research questions and inclusion/exclusion criteria, searching multiple databases (including Google Scholar, arXiv, PubMed, and SciSpace), screening titles/abstracts and full text, removing duplicates, extracting structured data, and compiling a report with sections and references. The presenter emphasizes that the agent can do much of the heavy lifting, but researchers remain responsible for oversight—checking steps, correcting omissions, and verifying outputs.

Beyond text-heavy research, the agent can run code against user-provided spreadsheets to generate visualizations and analysis, producing charts and then incorporating them into a LaTeX report. It can also build and deploy interactive websites by browsing a Google Scholar profile, extracting publication/citation metadata, and generating HTML pages with filters and sections—then iterating based on user feedback.

Pricing is handled through a credit system tied to task complexity and consumption, with systematic reviews typically costing “a few hundred credits,” while smaller tasks cost less. The webinar also addresses compliance concerns: plagiarism tools like Turnitin are framed as unlikely to flag rephrased AI-assisted writing, while AI-detection tools may still identify AI-generated text—so the recommended approach is to use agent outputs as drafts and rewrite in one’s own words.

Overall, the agent’s value proposition is speed with structure: long-running, research-specific automation that produces auditable intermediate artifacts, while keeping humans in the loop for correctness, originality, and final editorial control.