Creating API-Driven Workflows

I’ve been experimenting with ways to bring AI into the everyday work of telco — not as an abstract idea, but as something our teams and customers can use. On a recent build, I created a live chat agent I put together in about 30 minutes using n8n, the open-source workflow automation tool. No code, no complex dev cycle — just practical integration. The result is an agent that handles real-time queries, pulls live data, and remembers context across conversations. We’ve already embedded it into our support ecosystem, and it’s cut tickets by almost 30% in early trials. Here’s how I approached it: Step 1: Environment I used n8n Cloud for simplicity (self-hosting via Docker or npm is also an option). Make sure you have API keys handy for a chat model — OpenAI’s GPT-4o-mini, Google Gemini, or even Grok if you want xAI flair. Step 2: Workflow In n8n, I created a new workflow. Think of it as a flowchart — each “node” is a building block. Step 3: Chat Trigger Added the Chat Trigger node to listen for incoming messages. At first, I kept it local for testing, but you can later expose it via webhook to deploy publicly. Step 4: AI Agent Connected the trigger to an AI Agent node. Here you can customise prompts — for example: “You are a helpful support agent for ViewQwest, specialising in broadband queries – always reply professionally and empathetically.” Step 5: Model Integration Attached a Chat Model node, plugged in API credentials, and tuned settings like temperature and max tokens. This is where the “human-like” responses start to come alive. Step 6: Memory Added a Window Buffer Memory node to keep track of context across 5–10 messages. Enough to remember a customer’s earlier question about plan upgrades, without driving up costs. Step 7: Tools Integrated extras like SerpAPI for live web searches, a calculator for bill estimates, and even CRM access (e.g., Postgres). The AI Agent decides when to use them depending on the query. Step 8: Deploy Tested with the built-in chat window (“What’s the best fiber plan for gaming?”). Debugged in the logs, then activated and shared the public URL. From there, embedding in a website, Slack, or WhatsApp is just another node away. The result is a responsive, contextual AI chat agent that scales effortlessly — and it didn’t take a dev team to get there. Tools like n8n are lowering the barrier to AI adoption, making it accessible for anyone willing to experiment. If you’re building in this space—what’s your go-to AI tool right now?

APIs aren't just endpoints for data engineers - they're the lifelines of your entire data ecosystem. Choosing the Right API Architecture Can Make or Break Your Data Pipeline. As data engineers, we often obsess over storage formats, orchestration tools, and query performance—but overlook one critical piece: API architecture. APIs are the arteries of modern data systems. From real-time streaming to batch processing - every data flow depends on how well your APIs handle the load, latency, and reliability demands. 🔧 Here are 6 API styles and where they shine in data engineering: 𝗦𝗢𝗔𝗣 – Rigid but reliable. Still used in legacy financial and healthcare systems where strict contracts matter. 𝗥𝗘𝗦𝗧 – Clean and resource-oriented. Great for exposing data services and integrating with modern web apps. 𝗚𝗿𝗮𝗽𝗵𝗤𝗟 – Precise data fetching. Ideal for analytics dashboards or mobile apps where over-fetching is costly. 𝗴𝗥𝗣𝗖 – Blazing fast and compact. Perfect for internal microservices and real-time data processing. 𝗪𝗲𝗯𝗦𝗼𝗰𝗸𝗲𝘁 – Bi-directional. A must for streaming data, live metrics, or collaborative tools. 𝗪𝗲𝗯𝗵𝗼𝗼𝗸 – Event-driven. Lightweight and powerful for triggering ETL jobs or syncing systems asynchronously. 💡 The right API architecture = faster pipelines, lower latency, and happier downstream consumers. As a data engineer, your API decisions don’t just affect developers—they shape the entire data ecosystem. 🎯 Real Data Engineering Scenarios to explore: Scenario 1: 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Challenge: Process 100K+ transactions/second with <10ms latency Solution: gRPC for model serving + WebSocket for alerts Impact: 95% faster than REST-based approach Scenario 2: 𝗠𝘂𝗹𝘁𝗶-𝘁𝗲𝗻𝗮𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 Challenge: Different customers need different data subsets Solution: GraphQL with smart caching and query optimization Impact: 70% reduction in database load, 3x faster dashboard loads Scenario 3: 𝗟𝗲𝗴𝗮𝗰𝘆 𝗘𝗥𝗣 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Challenge: Extract financial data from 20-year-old SAP system Solution: SOAP with robust error handling and transaction management Impact: 99.9% data consistency vs. 85% with custom REST wrapper Image Credits: Hasnain Ahmed Shaikh Which API style powers your pipelines today? #data #engineering #bigdata #API #datamining

“Should OpenAPI be treated as supplementary documentation, evolve alongside requirements, or serve as the primary source of truth?” That was the sharp question one of my API PM students asked yesterday. So what’s the answer? API Descriptions—including OpenAPI Initiative, AsyncAPI Initiative, GraphQL Foundation schemas, and Protobufs—can sit at the center of every stage of the API lifecycle. Here’s how: 1. IDEATION & PROTOTYPING Use your API Description Document as a design artifact for Mock APIs. Share these prototypes with customers for discovery and validation. 2. DESIGN & DEVELOPMENT Treat the API Description Document as a Contract—validated against organizational standards—to ensure the final delivery aligns with the initial market-validated design. Enforce this with contract testing in your CI/CD pipeline to keep the contract and implementation in sync. 3. SECURITY Take that same API Description Document (which exactly matches the implementation because you’re testing it!) and run it through specialized vulnerability scanners to ensure you meet security and privacy standards. 4. DOCUMENTATION Your API Description Document doubles as the blueprint for interactive documentation in your Developer Portal. Involve your tech writers directly in that doc, then use it to render and continuously test your published docs so they never diverge from what’s actually delivered. 5. RUNTIME CONFIGURATION Gateways, Monitoring, and Analytics tools can ingest the API Description Document for consistent configuration across environments. Many top-tier tools already support this workflow. 6. CLIENTS Providing a public API Description Document lets your consumers easily generate SDKs and scaffold clients. So, check your API Descriptions into version control alongside your code and use them throughout the lifecycle as a living contract. And spoiler alert—AI is already enhancing every stage of the API lifecycle. We’ll dig into that in a separate post. Are you using API Description Documents as contracts today? My students and I would love to hear your real-world scenarios—drop them in the comments!

The whole point of agentic systems is not just about solving but automating complex workflows. Agentic workflows are quickly becoming the dominant paradigm for AI applications. Agentic workflows commonly coordinate multiple models and tools with complex control logic. What happens when you have to coordinate more complex processes that go beyond a single agent’s scope? This is where agentic workflows come into the picture. An agentic workflow is a multi-step, dynamic process that orchestrates multiple API calls, AI tasks, agents, and even human-in-the-loop steps within a dynamic control graph. The workflow can branch, loop, or change course based on AI-driven evaluations, allowing it to adapt in real time. Rather than embedding all logic inside a single agent, the workflow externalizes decision points and coordinates agents and services. Agentic workflows enable output validation, decision overriding, human oversight, and other observability features out-of-the-box. This is crucial for enterprise uses where governance over autonomous agents is needed. Example use cases: ➟ Threat detection pipelines ➟ Fraud or claims processing ➟ Research assistants coordinating search, summarization, and synthesis. Key elements: ➟ Task Nodes: AI agents, LLM tasks, API calls, database queries, manual review steps ➟ Decision Nodes: AI-driven logic for routing control flow. ➟ Working Memory: Shared state across workflow steps. ➟ Flexible Control Flow: Branching, looping, and fallback paths for dynamic control. Essentially, the workflow provides a structure within which the AI agent can choose different paths or repeat steps as needed. Know more about agentic workflows: https://lnkd.in/gKrJ3ddK Here is my practical guide on building agentic applications/systems: https://lnkd.in/gh5S8KiH Here is my hands-on guide on building agentic workflows: https://lnkd.in/ggCaDm7z

Step-by-Step Guide to Expose SAP CPI Integration Flows as APIs via API Management using SAP Integration Suite's API Management (APIM). 1. Set Up SAP Process Integration Runtime Navigate to: SAP BTP Cockpit → Subaccount → Instances & Subscriptions Create a Service Instance: Service: SAP Process Integration Runtime Plan: API Instance Name: e.g., CPI_API_Instance Roles: Assign all roles (except security roles) Create Service Key: Click the ⋮ (3-dot menu) → Create Service Key → Save the credentials (Client ID, Client Secret, Token URL) 2. Design & Deploy a Sample iFlow In Integration Suite: Create Package: Design → Integrations & APIs → Create Package (e.g., Demo_API_Package) Build iFlow: Add an HTTP Sender Add a Content Modifier (set sample body content) Deploy the iFlow Test: Use Postman to send a request to the iFlow endpoint → Validate the sample response 3. Configure API Provider with OAuth2 In API Management: Create API Provider: Configure → API Providers → Create New Name: e.g., CPI_Provider Connection Type: Cloud Integration Host: Use the host from the Service Key created earlier Authentication: Select OAuth2 Client Credentials → Enter Client ID, Client Secret, and Token URL 4. Create & Deploy API Proxy Create API Proxy: Select the API Provider (e.g., CPI_Provider) Click Discover Choose your deployed iFlow Enable OAuth and provide credentials from the Integration Flow instance Proxy Name: e.g., flow-api-proxy Save & Deploy → Copy the Proxy URL for testing 5. Test Your API Open Postman → Paste the Proxy URL → Send a request → Confirm the response from your iFlow With this setup, your SAP CPI iFlows can now be managed as full-fledged APIs using API Management in SAP BTP.

𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐰𝐢𝐥𝐥 𝐧𝐨𝐭 𝐬𝐜𝐚𝐥𝐞 𝐢𝐧 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐰𝐨𝐫𝐥𝐝 𝐢𝐟 𝐭𝐡𝐞𝐲 𝐜𝐚𝐧 𝐧𝐨𝐭 𝐭𝐚𝐥𝐤 𝐭𝐨 𝐭𝐨𝐨𝐥𝐬, 𝐀𝐏𝐈𝐬, 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐲. This is where MCP (Model Context Protocol) changes the game. Think of MCP as the “middleware” that lets AI Agents plug into anything databases, APIs, configs, files, or even other agents. But here is the kicker: it is not a one-size-fits-all model. 𝐓𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝟖 𝐂𝐨𝐫𝐞 𝐌𝐂𝐏 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐞𝐯𝐞𝐫𝐲 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐬𝐡𝐨𝐮𝐥𝐝 𝐤𝐧𝐨𝐰: --- 1. Analytics Data Access Pattern MCP connects AI agents to OLAP systems via tools, making large-scale analytics queries possible. Use case: Business intelligence, dashboards, and real-time insights. 2. Configuration Use Pattern AI agents fetch and apply configurations directly from config management services. Use case: Dynamic system tuning, feature flagging, multi-tenant app setups. 3. Hierarchical MCP Pattern Parent MCP servers orchestrate domain-level MCPs (payments, wallet, customer). Use case: Enterprise architectures where domains must stay modular but interoperable. 4. Local Resource Access Pattern Agents execute file operations (read, write, transform) through MCP tools. Use case: Enterprise workflows with on-premise or hybrid file processing. 5. Event-Driven Integration Pattern MCP streams events into async workflows for real-time decisioning. Use case: Fraud detection, IoT alerts, trading signals, ops monitoring. 6. MCP-to-Agent Pattern General AI agents delegate tasks to specialist agents via MCP. Use case: Connecting a customer service bot to a finance-specific expert agent. 7. Direct API Wrapper Pattern MCP tools wrap APIs, making complex API integrations simpler and uniform. Use case: AI agents querying multiple SaaS tools (CRM, HR, billing) in one flow. 8. Composite Service Pattern MCP orchestrates multiple APIs into one unified service layer. Use case: Multi-step workflows like booking + payments + notifications. --- 👉 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥𝐢𝐭𝐲: 𝐊𝐧𝐨𝐰𝐢𝐧𝐠 𝐭𝐡𝐞𝐬𝐞 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐭𝐨𝐲 𝐝𝐞𝐦𝐨 𝐚𝐧𝐝 𝐝𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐚 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐠𝐫𝐚𝐝𝐞 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦. 𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐌𝐂𝐏 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐰𝐢𝐥𝐥 𝐛𝐞𝐜𝐨𝐦𝐞 𝐭𝐡𝐞 𝐝𝐞𝐟𝐚𝐮𝐥𝐭 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐟𝐨𝐫 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝟏𝟐 𝐦𝐨𝐧𝐭𝐡𝐬? ♻️ Repost this to help your network get started ➕ Follow Jothi Moorthy for more #AI #MCP #AIagents #SystemDesign

This guy literally shared a step-by-step roadmap to build your first AI agent, and it's absolute 🔥 Text version: **1. Pick a very small and very clear problem** Forget about building a “general agent” right now. Decide on one specific job you want the agent to do. Examples: * Book a doctor’s appointment from a hospital website * Monitor job boards and send you matching jobs * Summarize unread emails in your inbox The smaller and clearer the problem, the easier it is to design and debug. --- **2. Choose a base LLM** Don’t waste time training your own model in the beginning. Use something that’s already good enough: * GPT * Claude * Gemini * Open-source options like LLaMA and Mistral (if you want to self-host) Just make sure the model can handle reasoning and structured outputs, because that’s what agents rely on. --- **3. Decide how the agent will interact with the outside world** This is the core part people skip. An agent isn’t just a chatbot — it needs tools. You’ll need to decide what APIs or actions it can use. A few common ones: * Web scraping or browsing (Playwright, Puppeteer, or APIs if available) * Email API (Gmail API, Outlook API) * Calendar API (Google Calendar, Outlook Calendar) * File operations (read/write to disk, parse PDFs, etc.) --- **4. Build the skeleton workflow** Don’t jump into complex frameworks yet. Start by wiring the basics: * Input from the user (the task or goal) * Pass it through the model with instructions (system prompt) * Let the model decide the next step * If a tool is needed (API call, scrape, action), execute it * Feed the result back into the model for the next step * Continue until the task is done or the user gets a final output This loop — model → tool → result → model — is the heartbeat of every agent. --- **Extra Guidance** 1. Add memory carefully Most beginners think agents need massive memory systems right away. Not true. * Start with just short-term context (the last few messages). * If your agent needs to remember things across runs, use a database or a simple JSON file. * Only add vector databases or fancy retrieval when you really need them. 2. Wrap it in a usable interface CLI is fine at first. Once it works, give it a simple interface: * Web dashboard (Flask, FastAPI, or Next.js) * Slack/Discord bot * Script that runs on your machine The point is to make it usable beyond your terminal so you see how it behaves in a real workflow. 3. Iterate in small cycles Don’t expect it to work perfectly the first time. * Run real tasks. * See where it breaks. * Patch it, run again. Every agent I’ve built has gone through dozens of these cycles before becoming reliable. 4. Keep the scope under control It’s tempting to keep adding more tools and features. Resist that. --- Need an AI Consultant or help building your career in AI? Message me now

Ever feel like the more “automation tools” you add, the more tangled and expensive your workflows get? You’re not alone. Most teams end up stitching together Zapier, Power Automate, and a dozen other tools just to stay afloat. The result? • Logic scattered across platforms • Extra costs and slower performance • No visibility for the people doing the actual work This is exactly the problem Process Street set out to solve in our latest update. Now imagine this: ✅ AI Tasks: Let AI handle the boring stuff like document summaries, translations, data extraction, email writing, and routing. All inside your workflow. Every step is human-approved and fully auditable. ✅ Code Tasks: Need calculations, dynamic logic, or API calls? Just write native JavaScript directly in your workflow. No middleware or fragile glue code. Real example: A Salesforce deal triggers onboarding across five markets. AI handles the documents. Code handles the pricing. The humans review and approve with full visibility. If you use tools like Jira, SharePoint, BambooHR, or Salesforce, everything syncs in real-time both ways. If you're scaling and tired of tech sprawl, just comment “Smart Tasks” and I’ll DM you a cheatsheet and templates from our latest session. Workflows should feel like clarity, not chaos. We can help you get there. See how automated workflows can transform your business: process.st

Automation without limits – My take on n8n In today’s world, time is our most valuable asset. That’s where n8n comes in — an open-source workflow automation platform that connects hundreds of apps and lets you build powerful workflows with zero (or minimal) code. What excites me about n8n: ✅ Visual workflow builder (no need to reinvent the wheel) ✅ Flexible — self-host or run in the cloud ✅ 400+ integrations (APIs, databases, AI tools, messaging apps, CRMs…) ✅ Perfect for automating repetitive tasks, syncing data, and even posting on LinkedIn 😉 Getting Started with n8n for LinkedIn Automation 1. Choose Your Workflow Template • Notion-based → straightforward templated posting • GPT‑4 → content automation + group distribution • Gemini + image → content with visuals 2. Set Up Authentication • Connect your LinkedIn via OAuth. • For AI workflows, connect Google Sheets, GPT‑4, Google Gemini, or image generation API as needed. 3. Customize Flow • Map your fields, prompts, and styling rules. • Add manual approval nodes if you’d prefer a “review then post” approach. 4. Test & Deploy • Test with sample entries. • Once everything works, activate your workflow for daily or as-needed posts. For example, you can: • Generate a LinkedIn post with AI → Review → Auto-publish • Sync Notion content → Distribute directly to LinkedIn • Automate notifications, reporting, and more In short: n8n lets individuals and teams work smarter, not harder. 👉 Have you tried automating your daily workflows yet? What’s the one task you wish was automated today? #Automation #n8n #Productivity #AI #OpenSource

So, you joined a hot AI startup thinking you'd build agents. But instead, you're working on repetitive API plumbing tasks: - Constantly wrapping APIs into function calling - Handling rate limits, retries, and telemetry - Managing complex auth flows - Patching version mismatches across services - Drowning in tool wrappers Today, building and managing the plumbing that connects Agents to APIs feels like a full-time job. Developers need to: - Handle authentication for each API - Write detailed descriptions for tool discovery - Design schemas designed for LLM compatibility - Integrate tools created by 3rd parties developers - Hope none of the tooling comes with inherent security risks High quality AI experiences come down to two key elements: 𝗠𝗼𝗱𝗲𝗹 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 + 𝗧𝗼𝗼𝗹 𝗤𝘂𝗮𝗹𝗶𝘁𝘆. That's why AI <> API integrations need to: 1. Leverage OpenAPI for effortless API consumption 2. Allow developers to remix and tune tools with curation features like variations and toolsets 3. Instantly expose toolsets as hosted MCP servers 4. Make tools available to users in Slack as SDKs 5. Free developers from worrying about authentication across APIs 2025 is not the year to build plumbing. It's why we built 𝗚𝗿𝗮𝗺 getgram.ai 👉🏽👉🏽👉🏽 https://lnkd.in/ebQfq2P4 Leveraging Speakeasy’s unique integration with OpenAPI, the Gram platform makes creating and managing high quality AI tools effortless. Chat and build agentic workflows with your APIs using the link in the comments. #speakeasy #mcpserver #api