Learn AI · Research
Research workflows, run from one command.
The most underrated part of an AI terminal: it is not a chatbot, it is a research lab. Here is what the built-in workflows do and when to use each.
Deep research, with citations
expert-ai deepresearch "the economics of modular construction"
The flagship workflow. It plans a multi-source investigation, gathers evidence, and produces a durable research brief with inline citations and a provenance sidecar — every claim marked as verified, unverified, inferred, or blocked. This is research-grade rigor, not a chat answer.
Literature reviews
expert-ai lit "test-time scaling laws"
Search papers and synthesize primary sources into a structured review. Good for survey chapters, grant background, and "what has anyone actually done here?" questions.
Paper audits & replication
| Command | What it does |
|---|---|
expert-ai audit <paper> | Compares a paper's claims against its public code; flags mismatches and reproducibility risks. |
expert-ai replicate <paper> | Plans or executes a full replication workflow. |
expert-ai review <artifact> | Simulates a tough peer review with objections, severity, and a revision plan. |
expert-ai compare <topic> | Source-grounded comparison matrix with confidence levels. |
Autonomous loops & watches
autoresearch— try an idea, measure results, keep what works, discard the rest, repeat.watch— a recurring research watch on a topic, company, or paper area.draft— turn findings into a polished paper-style draft with equations and citations.code— full-spectrum software engineering and technical automation.
The 5-agent engine behind the workflows
Expert AI Terminal runs 5 specialized parallel agents that collaborate in one session — the core reason workflows can run autonomously:
| Agent | Role |
|---|---|
| Researcher | Gathers evidence, reads sources, explores the web and your vault. |
| Reviewer | Checks the work against criteria and catches gaps. |
| Writer | Drafts the deliverable in a polished, structured form. |
| Verifier | Confirms citations and provenance — marks claims verified/unverified/inferred. |
| Architect | Plans the overall approach and keeps the session on track. |
Add the 33+ specialized tools (file read/write, repo management, web search, sandboxed execution, Obsidian writes) and you have what ssv.asia calls "an end-to-end environment that turns a language model into a disciplined, reproducible engineering team".
Coding agents & harnesses (the 2026 wave)
2026's trend is agentic development: coding agents that can edit files and run commands, wrapped in secure harnesses that provide sandboxed runtimes, API gateways, and unified session state. The terminal is exactly this — ssv.asia calls it a "purpose-built agentic harness… it doesn't just chat — it reads and edits your local files, runs commands, and learns".
- One-click launch — a .bat/.command launcher gets you a ready-to-run terminal.
- Real-time transparency — see reasoning steps, approve or reject edits inline.
- Integrated test bot — auto-creates unit tests, runs them sandboxed, reports pass/fail.
- Policy-driven security — per-project rules like "no external SDK calls".
- Persistent knowledge — style guides and library versions stay consistent across sessions.
Dive deeper on ssv.asia's 2026 AI trending agents & harnesses article ↗.
Why this matters for beginners
Most people start with chat and ask one question at a time. Workflows flip the model: you describe a goal, the agent runs a multi-step plan, and hands you a durable artifact you can cite, edit, and build on. You learn the craft of research, not just the craft of asking.