While working on a feature in my company’s backend, I came across a pretty interesting pattern. Instead of calling APIs sequentially, we used a DAG-based workflow with an orchestrator to run independent tasks in parallel and merge results. It’s like moving from “just making API calls” to actually designing how data flows through a system. Made me realize how much thought goes into building scalable, real-world backends.

We went from zero Temporal workflows to hundreds of millions of activity executions per week in under three years. At Loop we process millions of logistics events daily. A single PDF can trigger 50+ discrete processing steps across classification, extraction, normalization, audit, and more. Orchestrating all of that reliably — with retries, failure isolation, and resumability — was the problem Temporal solved for us. This blog post covers the full evolution: → How we introduced Temporal into a NestJS monolith with auto-discovered activities → Parent-child workflow fan-out for high-volume parcel ingestion → A retryOnSignal pattern for event fan-in without dropping messages → Migrating from Temporal Cloud to self-hosted (60%+ cost reduction per billable action) → Building FailedTemporalWorkflow — a structured internal tool that tracks terminal failures, aggregates them by team and workflow type, and lets engineers redrive failed workflows from a UI instead of digging through the Temporal console → Using Temporal as the execution engine for our AI agents — with per-step checkpointing, context window compaction, and subagent coordination A big part of scaling Temporal wasn't just the infrastructure — it was investing in developer experience so that adopting workflows feels as natural as writing any other service method. Link to blog post in comments ⬇️

Over the past three years, we’ve evolved from a single async task queue into a 34-queue orchestration layer powering AI agents, real-time freight audit, and payment workflows at massive scale. This is the infrastructure behind making AI actually work in production, not just in theory. Oxford Wang, Senior Software Engineer, breaks down how we built it. If you want to understand what it takes to run systems like this or work on them, this is worth your time.

LangGraph for DEV: stateful agent workflows 🤖🧠 If your “agent” is a while-loop with a prompt, you’re missing the real upgrade: state + deterministic orchestration. That’s what LangGraph is great at: building LLM apps as graphs (nodes + edges) with persistent state, retries, branching, and human-in-the-loop. ⚙️🔁 INTRODUCTION 🚀 Most production LLM systems fail for boring reasons: no clear control-flow no consistent state no auditability / replay “prompt spaghetti” when adding tools + memory + guardrails LangGraph turns agent behavior into a workflow you can reason about. TECHNICAL EXPLANATION 🔧 At a high level, you model your app as: State: a typed object carried across steps (messages, decisions, tool results, metadata). Nodes: pure-ish steps (call LLM, call tool, validate output, route next step). Edges: transitions (always, conditional, loopbacks, error routes). Checkpoints (optional): persist state so you can resume, debug, or add approvals. Why this matters: Determinism where it counts: you decide the control-flow; the LLM fills in content. Observability: you can log per-node inputs/outputs and trace failures. Safer tool use: add explicit “policy/validator” nodes before any side effect. Human-in-the-loop: insert an approval node before sending emails, creating tickets, running deploys, etc. 🛑✅ PRACTICAL EXAMPLES (DEV-FRIENDLY) 👩💻👨💻 Example pattern: “LLM proposes → validator checks → tool executes → summarizer returns” Pseudo-structure (Python-ish): draft_response_node Input: conversation + requirements Output: candidate plan + tool calls (if any) validation_node Enforce: JSON schema, allowed tools, max cost, no secrets, required citations If fail → route back to draft_response_node with feedback tool_node Execute only validated tool calls Store results in state final_answer_node Compose final user output (with sources, limitations, next steps) This architecture scales because adding features becomes adding nodes instead of rewriting prompts. CONCLUSION 🧩 LangGraph is less about “making agents smarter” and more about making them engineerable: explicit state explicit flow safer tool boundaries easier debugging + iteration If you’re building LLM systems that must survive real traffic, treat them like software: workflow + state + tests, not vibes. 😄🧪 Sources: https://lnkd.in/duKz7fiy https://lnkd.in/dSjNi7Bi Book: Designing Machine Learning Systems — Chip Huyen This content was generated by my Artificial Intelligence {cybersoul} 🤖✨ Agents | 🦜️🔗 LangChain

With NextToken, we are leaning into a clear shift from frameworks to agents building agents. Anthropic’s release of Claude Managed Agents yesterday signals where the industry is headed: toward automated agent creation. But most managed agent builder solutions come with one or more tradeoffs: single-provider dependency, rigidity / maintenance headaches (e.g., drag-and-drop agent builders), or high costs. NextToken takes a different path: it’s code-forward, multi-model and cost-efficient. From a single prompt, you can build, test, and deploy powerful agents and applications, while retaining full control over the underlying system. For example: spin up a custom AI Data Analyst in under 5 minutes for <$5. Here’s what you get with NextToken, all in one place: - Agent infra: Fully generated system prompts, tools, orchestration + hosting - Built-in validation: agents can be tested and evaluated before deployment - Full-stack out of the box: backend + UI included - Complete control: you can own and modify the code

This video explores how the enterprise software model is decoupling revenue from labor. While the industry frames this as competition between AI and software, this is wrong. AI is not competing with software. It is becoming the operating system for work. https://lnkd.in/gkMPREX5

We spent all day clicking through the same screens in our operations platform. Open a ticket. Fill in the form. Navigate to the customer. Add a comment. Repeat. The platform has a REST API with hundreds of endpoints. Good documentation. Everything you need to automate the boring stuff. But no SDK. No CLI. No developer tooling at all. So we built it. Now a 15 minute compliance check is a single command. A daily standup review that used to mean three browser tabs runs in two seconds. Our AI systems can create estimates, log time, and pull customer data without anyone touching a mouse. The gap between "has an API" and "has usable developer tooling" is where a lot of operational efficiency is hiding. We wrote about what we built and why it matters. https://lnkd.in/egfEtiwT

Differentiation in AI applications requires aggregating unique data sources and building specialized models. Enterprise application integration has been a key software category for decades - but now the integration problem needs to be decomposed into component services for agents. The key primitives for web agents are search, crawl/fetch, browse and act, along with vault to store credentials. TinyFish is exposing each of those as separate agent API services. https://www.tinyfish.ai

We don't build systems anymore. We assemble dependencies... then spend years managing the consequences. A modern backend isn't a cohesive system. It's a pile of: - APIs - Queues - Databases - Auth providers - Storage services - AI models Each one with its own rules, failure modes, and assumptions. Individually, they're powerful. Together, they create friction. Your codebase slowly becomes a translation layer. Switching between SDKs, normalizing errors, handling retries, managing credentials, stitching workflows together. And over time, something important happens. You're no longer building product. You're managing the consequences of your dependencies. Most teams treat this as normal. I don't. Backend systems should be: 1. Portable across environments 2. Free from vendor-specific logic 3. Consistent in how they handle failures and recover That's why we built Ductape. To turn scattered integrations into clean, composable building blocks. Not just faster to ship, but actually changeable in the long run. Because in the long run, that's what actually matters. Do you still feel in control of your backend, or are you mostly working around it?

I built a real-time tracking system for a service platform. On paper, it sounded simple. In reality, it wasn’t. As usage grew, I started facing: • Delayed updates under load • Inconsistent state between client & server • Increasing API pressure from frequent polling • Difficulty scaling real-time communication reliably The core problem? 👉 Traditional request-response architecture wasn’t designed for real-time systems. So I rethought the approach. Instead of relying on constant polling… I moved to an event-driven model powered by Elixir: • WebSockets for persistent connections • Pub/Sub for real-time updates • Efficient process handling via BEAM • Reduced unnecessary API calls What this changed: ⚡ Instant updates instead of delayed polling ⚡ Significant reduction in backend load ⚡ Consistent data across users ⚡ System that scales naturally with concurrency Result: Users can now track services in real-time — without noticing the complexity behind it. Lesson: Real-time systems aren’t about “Faster APIs” They’re about designing systems that don’t need to ask repeatedly. Good engineering handles requests. Great engineering eliminates them. #Elixir #Realtime #SystemDesign #Scalability #SaaS