> For the complete documentation index, see [llms.txt](https://docs.agenticflow.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.agenticflow.ai/changelog/office-hour-46-recap-and-workflow-blueprint-prompts.md). # Office Hour #46: Recap & Workflow Blueprint Prompts ### 📺 Quick Recap April's frontier model wave landed fast. In Office Hour #46, Sean walked through **Claude Opus 4.7, GPT-5.5, and DeepSeek V4 Pro + V4 Flash** inside AgenticFlow, then stress-tested them with a one-prompt **ISS Orbital Tracker** demo. We also covered: * **Time-aware workspace updates** – timezone standardization & Tasks-tab timestamps * **Advanced MCP authentication** & Composio MCP deprecation * **Stability fixes** across the platform * **DeepSeek migration notes** – legacy DeepSeek models should migrate to **DeepSeek V4 Flash before May 31, 2026** * **6 practical Claude Code + AgenticFlow CLI workflow demos** for everyday business automation 🎥 Watch the full session: {% embed url="" %} **Overview:** April brought a back-to-back cluster of frontier model launches — **Claude Opus 4.7** (16 Apr), **GPT-5.5** (23 Apr), and **DeepSeek V4 Pro + V4 Flash** (24 Apr). All four are wired into AgenticFlow within days of release, so you can swap them into existing agents and workflows without changing a line of configuration. On the engine side, this release continues the timezone work documented in #45, ships the advanced MCP auth options builders have been asking for, and removes Composio MCP after upstream routing changes. ### 🧠 New Frontier Models * **Claude Opus 4.7 — the new coding leader.** Anthropic's April release jumps to **87.6% on SWE-bench Verified** (up from 80.8% on Opus 4.6) and **64.3% on SWE-bench Pro**, putting it ahead of GPT-5.4 (57.7%) and Gemini 3.1 Pro (54.2%) on real-world software engineering. Reach for Opus 4.7 as the default model on long-horizon agent loops, multi-file refactors, code review, and any workforce slot where the agent needs to plan, call tools repeatedly, and recover from its own mistakes. Same $5/$25 per-million pricing as Opus 4.6. * **GPT-5.5 — the operator and tool-call champion.** OpenAI's late-April release dominates agentic execution benchmarks: **82.7% on Terminal-Bench 2.0** (vs. Opus 4.7 at 69.4% and Gemini 3.1 Pro at 68.5%), **78.7% on OSWorld-Verified**, and **98.0% on Tau2-bench Telecom** for customer-service workflows. Available via the standard OpenAI provider at $5/$30 per million with a 1M-token context window. Best when an agent has to drive a real shell, click through web environments, or run long customer-facing dialog trees. * **DeepSeek V4 Flash on the AgenticFlow provider — the new default.** The 284B/13B-active model is available directly under our AgenticFlow provider with no extra setup, and its quality is already at the peak we'd previously have split between two tiers: **79.0% on SWE-bench Verified**, **91.6% on LiveCodeBench**, and reasoning strong enough to retire the old `deepseek-reasoner` slot. Use V4 Flash as your default DeepSeek model — chat, coding, reasoning, bulk classification, content generation, lightweight tool-using agents — there's no longer a reason to split. * **DeepSeek V4 Pro on the PixelML provider — frontier-tier for specialized work.** New 1.6T-parameter MoE (49B active), lands as the **#2 open-weights reasoning model** on the Artificial Analysis Intelligence Index. The efficiency story: at 1M-token context, V4 Pro uses **\~27% of the inference FLOPs and \~10% of the KV cache** compared to V3.2. Reach for V4 Pro only when you have a specialized frontier-tier workload — extreme long-context reasoning, research-grade analysis, or evals where the very top of the open-weights leaderboard matters. For day-to-day work, V4 Flash is the right call. * **Practical heads-up on DeepSeek V4.** DeepSeek's own evals show V4 Pro / V4 Flash answer with high confidence even when they don't know (94–96% answer rate on AA-Omniscience). Pair them with retrieval (Knowledge tab) or grounding tools when factual accuracy is load-bearing. * **⚠️ Legacy DeepSeek models retiring 31 May 2026 (PST).** The previous-generation API model IDs — `deepseek-chat`, `deepseek-reasoner`, and the **DeepSeek V3.2 / V3.2 Speciale / V3.2 Exp** entries — are scheduled for retirement after **May 31, 2026 (PST)**. Migrate every one of them to **DeepSeek V4 Flash** — its quality already covers both the previous chat and reasoner slots, so there's no need to split your fleet. Reach for **V4 Pro** only when you have a specialized frontier-tier workload that genuinely needs the larger model. The DeepSeek base URL and your existing API key stay the same — only the model field changes.
### 🌍 Global Consistency & User Experience * **Platform-wide timezone standardization.** We've completely overhauled how time is calculated and displayed across the platform. Every timestamp now dynamically syncs with your browser's local timezone instead of defaulting to UTC — drastically reducing confusion when reading chat history, scheduled triggers, or task logs. * **Precise chat history tracking.** Messages and activity logs in Chat History are now mapped to your exact local time down to the minute. When an agent runs a scheduled task, the time reflected in the chat matches your real-time expectation — no mental math, no UTC offset arithmetic.
* **Streamlined Tasks-tab timestamps.** Automated task execution logs in the new Tasks tab now mirror the same local timezone used in the chat interface. Cross-referencing "when did this trigger actually fire" against a chat thread is finally a single glance instead of a calculation. #### 🔐 Integrations & Security * **Advanced MCP authentication protocols.** Building custom integrations is now more flexible: you can configure API key authentication directly via custom HTTP headers (e.g. `x-api-key: your_key_here`) rather than being locked into a single bearer-token shape. This unblocks every internal API and self-hosted MCP server that uses provider-specific header conventions.
* **Composio MCP deprecated.** We removed the Composio MCP integration after the provider changed their core routing endpoints. If you had Composio-routed tools in a workflow, swap them for the equivalent direct integration — most are already available natively in the Marketplace. #### 🐞 Bug Fixes & Stability * **Knowledge retrieval setting now persists on reload.** Fixed a state mismatch in Agent settings where toggling "Automatic Knowledge Retrieval" to ON would visually reset to OFF on a browser tab reload, even though the value was correctly saved server-side. The UI now reflects the true database state on every reload.
* **Workflow `.json` round-trip integrity.** Fixed a serialization issue affecting workflow migration. Workflows downloaded as `.json` from AgenticFlow now upload back into any other workspace cleanly, preserving node fields through the import flow.
*** ### 🚀 6 Workflow Blueprint Prompts Copy any of the prompts below into **Claude Code** (with the **AgenticFlow CLI** installed) to deploy a fully working workflow in minutes — no manual node-wiring required. #### 1. Email → Structured Data > Using the AgenticFlow CLI, deploy the email-to-structured blueprint. Use this sample email as input: > > "Hi, I ordered the Pro plan last Tuesday (order #48291) and need help locating the activation key for my team. Can someone point me to the next step? — James" > > Deploy, run once, show me the extracted JSON (intent, urgency, category, entities, summary, suggested action). > > Leave the workflow deployed. Print the Web UI link. One-line note: why this rung of the composition ladder? *** #### 2. Competitor URL Snapshot > Using the AgenticFlow CLI, deploy the competitor-url-snapshot blueprint. Use [https://notion.so](https://notion.so/) as the competitor URL. Set your product to "a lightweight project management tool for solo founders". > > Deploy, run once, show me the positioning analysis, top 5 claims, weaknesses, and 3 counter-moves. > > Leave the workflow deployed. Print the Web UI link. One-line note: why this rung of the composition ladder? *** #### 3. Job Application Package > Using the AgenticFlow CLI, deploy the job-app-package blueprint. Use this job description: > > "Senior Software Engineer - Payments Infrastructure at Stripe. Requirements: 5+ years SWE experience, Python/Go/Java, distributed systems, AWS/GCP, track record scaling to millions of requests. Nice to have: fintech/payments domain, PCI-DSS, Kubernetes." > > CV: "Jane Doe. Senior SWE at Acme Corp 2021-2024: built payment microservices 50K txns/day in Python/FastAPI, migrated monolith to AWS Lambda -40% latency, mentored 3 engineers. SWE at StartupXYZ 2019-2021: Node.js APIs, Postgres, Kafka. BSc CS NUS 2019. Skills: Python, TypeScript, AWS, Postgres, Docker, Kubernetes." > > Recipient email: your own. Tone: confident. > > Deploy, run once, show me the fit score and cover letter draft. > > Leave the workflow deployed. Print the Web UI link. One-line note: why this rung of the composition ladder? *** #### 4. Meeting Notes → Recap Email > Using the AgenticFlow CLI, deploy the meeting-notes-email blueprint. Use these raw meeting notes as input: > > "Q2 planning, attendees: sarah (PM), dan (eng), mia (design). decided to cut mobile app from roadmap - too much dev time. dan will finish auth by friday. mia to redo onboarding screens by end of month. need to agree on pricing before june 1. sarah owns pricing doc. next meeting in 2 weeks." > > Meeting title: Q2 Planning. Recipient: . > > Deploy, run once, show me the structured recap email that would be sent. > > Leave the workflow deployed. Print the Web UI link. One-line note: why this rung of the composition ladder? *** #### 5. Lead Qualifier > Using the AgenticFlow CLI, deploy the lead-qualifier blueprint. Qualify this lead: > > Company: Linear ([https://linear.app](https://linear.app/)) Your product: "an AI-powered sprint planning tool for engineering teams" ICP: "Series B+ tech companies, 20-200 engineers, US/EU" > > Deploy, run once, show me the fit score, pain-point alignment, budget signals, and recommended outreach angle. > > Leave the workflow deployed. Print the Web UI link. One-line note: why this rung of the composition ladder? *** #### 6. Customer Feedback Digest > Using the AgenticFlow CLI, deploy the customer-feedback-digest blueprint. Use this batch of feedback: > > "Love the product, but the mobile app login flow needs polish — 3 stars" "---" "Onboarding was confusing; I needed clearer steps for connecting my calendar — 2 stars" "---" "Best tool I've used this year, saves me 3 hours/week on reports — 5 stars" "---" "I need clearer in-app guidance before upgrading teams — 3 stars" "---" "The AI summaries are useful, but I want more control over tone — 4 stars" > > Product name: MyApp. > > Deploy, run once, show me the sentiment breakdown, top themes, and product recommendations. > > Leave the workflow deployed. Print the Web UI link. One-line note: why this rung of the composition ladder? *** ### 🔗 Get Started * **Try AgenticFlow free:** * **CLI reference:** * **Latest changelog:** * **Join our Discord:** * **Found a bug?** Email with a reproducible case — the team will prioritize it.