"Research our top five competitors, extract their pricing, and build me an updated comparison deck." That sentence used to describe a week of work split across three people. In TESS, it is one prompt.
From answers to execution
Classic LLM chat generates text. Agentic chat generates a plan — then executes it. When you send an instruction, TESS:
- Decomposes the goal into discrete operations: browse these sites, extract these tables, monitor this news, write this document.
- Selects the right skill and model for each step. With 250+ models available, a reasoning-heavy step and a formatting step do not need the same engine.
- Chains the outputs. Extracted pricing data flows into the comparison table; the table flows into the slide builder; the deck lands back in your chat.
- Reports what it did. Every step is inspectable, so you can audit the chain instead of trusting a black box.
Why chaining matters more than any single skill
Individually, web browsing or document writing are commodity features. The compounding value comes from the chain: a 10-step workflow that previously required a human to shuttle context between tools now runs unattended. That is the difference between saving minutes and saving days.
Real chains our customers run daily
- Sales: company research → financial data → annual report reading → executive one-pager, ready 10 minutes before the call.
- Finance: currency and index data → news driving the movements → morning brief before the first meeting.
- HR: folder of 50 resumes → skill extraction → ranked shortlist with per-candidate summaries.
Explore the full playbooks library to see 40 ready-to-run workflows, or try one yourself.