EmilySun621 opened a new pull request, #5096: URL: https://github.com/apache/texera/pull/5096
**[This is still be under testing]** The Story Meet Dr. Sarah, a biomedical researcher at UCI. She studies diabetes. She's brilliant at biology but doesn't write code. She opens Texera and sees one generic AI agent. She asks it to build a workflow. The agent generates something — but it uses the wrong methodology, skips evaluation metrics, and doesn't follow the CRISP-DM framework her lab requires. She spends an hour fixing things the AI should have known. Next week, her student asks the same agent the same question and gets different, equally wrong results. This is the problem we solved. What If Researchers Could Build Their Own AI Agents? We built a Custom Agent Library — think of it as a "GPTs Store" for data science workflows. Dr. Sarah creates a "🧬 Diabetes Research Agent" in 2 minutes: Domain: Biomedical Methodology: CRISP-DM (her lab's standard) Guardrails: always split train/test, always evaluate models, never leak data Custom rules: "Always compare at least 2 models. Use logistic regression as baseline." Knowledge base: uploads her lab's data dictionary and methodology guide Model: Claude Opus for complex workflow generation Now when she asks "predict diabetes onset from my patient data," the agent already knows her standards. It generates a proper CRISP-DM workflow with train/test split, two competing models, and evaluation metrics — every time. She shares the agent with her students. They all get workflows that meet the lab's standards. No more fixing AI mistakes. No more inconsistency. But What Happens When Things Go Wrong? Her student accidentally deletes half the workflow. Or the AI agent modifies something it shouldn't have. In the old Texera, there's just a version number and a timestamp. No way to know what changed, who changed it, or how to get back. We built a Workflow Time Machine — version control for workflows, like Git but visual. Every meaningful change is captured: when the AI generates operators (🤖), when a user edits manually (👤), when someone clicks Run. Not every keystroke — smart snapshots every 5 minutes, on execution, and on manual save. The student sees the timeline: "Oh, the AI added Random Forest at 5:30, then I accidentally deleted the evaluation at 5:35." One click on Revert, and the workflow snaps back. Crisis averted. The AI agent can even use the Time Machine — "undo my last 3 changes" just works. What We Built Custom Agent Library "Agents" page in the sidebar — create, configure, browse, share specialized agents Full customization — domain, methodology, guardrails, knowledge base, example workflows, model selection, output preferences Agent-aware chat — each agent has its own conversation history per workflow 163 built-in operators injected into the agent's prompt — agents use Texera's native operators, not Python UDFs Model selection — choose Claude Opus for complex tasks, Haiku for simple ones Workflow Time Machine Smart snapshots — auto-save every 5 minutes, on Run, on manual save. No noise. Rich timeline — who changed what, when, and whether it was human or AI One-click revert — go back to any version instantly Version diff — compare two versions to see what was added, removed, or modified Agent integration — the AI can browse history and revert changes via natural language Why This Matters Everyone else at this hackathon built one agent that does one thing. We built a platform where users create their own agents that do anything — and a safety net (Time Machine) so they never lose their work. For biomedical researchers who aren't programmers, this is the difference between "AI that sometimes helps" and "AI that works the way my lab works, every time." Demo Create a "🧬 Diabetes Research Agent" with CRISP-DM, guardrails, custom rules Ask it to build a classification workflow → proper DAG appears on canvas with built-in operators Open Time Machine → see 🤖 agent snapshot Make a mistake → revert with one click Compare two versions → see exactly what changed Technical Details Frontend: Angular components for Agent Library page, Create/Edit Agent dialog, agent selector in chat panel, Time Machine panel with timeline UI Backend: Custom system prompt builder with operator catalog injection (agent-service, TypeScript) workflow_snapshot table + REST endpoints for Time Machine (amber, Scala) Agent tool for version control (workflow-history-tool) No modifications to Texera's core engine (Amber). Everything is additive — new modules, new endpoints, new components. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
