Chase Agents blog

Insights on AI automation, agentic workflows, and operations intelligence from the Chase Agents team.

• Why did Anthropic ban OpenClaw? Here's the full story. — My cofounder messaged me this morning. "Claude just banned OpenClaw. Can they ban other products too? Can they ban us?" He's not technical. He builds the business side. And his reaction is probably th...
• 12 Real AI Agent Examples and How They Work (2026) — An AI agent is software that uses a language model to pursue a goal by planning steps and taking actions with tools — not just answering a question. Where a chatbot responds, an agent decides what to d...
• What Is an AI Agent Platform? How to Choose One in 2026 — An AI agent platform is software for building, deploying, running, and managing AI agents that take actions with your tools and data — without you assembling and hosting the infrastructure yourself. Wh...
• AI Automation Agency: What They Do, Cost & Alternatives (2026) — An AI automation agency is a service business that designs, builds, and maintains AI-powered workflows for other companies. They identify repetitive processes, connect the relevant apps, add AI where i...
• AI Sales Agent: What It Is, Real Workflows & How to Deploy One — An AI sales agent is software that automates parts of the sales process — such as prospect research, personalized outreach, lead qualification, and follow-up — by using AI to take actions across your t...
• Best Workflow Automation Tools in 2026: An Honest Comparison — The best workflow automation tools in 2026 are Zapier for breadth of simple integrations, Make for visual multi-step scenarios, n8n for self-hosted developer control, and Chase Agents for AI-built auto...
• Best Open-Source AI Agent Frameworks in 2026 (Honest Guide) — An open-source AI agent framework is a free, self-hostable toolkit for building agents that plan and take actions with tools, giving you full control over the code, models, and data. The trade-off for ...
• What Is an MCP Server? Meaning, How It Works & Examples — An MCP server is a program that exposes tools, data, and actions to AI applications through the Model Context Protocol (MCP), an open standard introduced by Anthropic. Instead of every AI client buildi...
• 7 Best Zapier Alternatives in 2026 (Honest Comparison) — The best Zapier alternatives in 2026 are Make for visual multi-step flows, n8n for self-hosted control, and Chase Agents for plain-English automations that run deterministically. People usually look fo...
• The saga pattern: your undo button is a clever forgery — A six-step automation runs cleanly for months. Then one Tuesday the fourth step times out and the run stops with a red error icon. The real problem is everything that already happened before it stopped...
• n8n vs Make: per-step billing is the real decider here — Most n8n vs Make comparisons spend two thousand words on the wrong variable. They count integrations, screenshot the drag-and-drop canvas, and hand out points for "ease of use." None of that tells you ...
• Exponential backoff with jitter: add more than you think — Open almost any retry helper and you will find the same line: wait 2 ** attempt seconds, then add a few hundred milliseconds of random noise. That noise is jitter, and the instinct behind it is right. ...
• MCP server vs API: when the wrapper is just overhead — The phrase "mcp server vs api" sets up a fight that does not exist. An MCP server is not a rival to your API. It is a thin layer that sits on top of one and describes it so a language model can read th...
• The LangChain ReAct agent you import is deprecated — If you copied from langgraph.prebuilt import create_react_agent out of a tutorial written in 2024 or early 2025, the import still runs, but it now prints a deprecation warning. For anyone searching for...
• Reranking in RAG: why retrieval depth beats the model — Reranking in RAG has a reputation as the quick win. Bolt a cross-encoder onto the pipeline, the story goes, and mediocre retrieval turns sharp. Half of that holds up. A reranker does sharpen results, b...
• OpenAI function calling: why schema-valid calls still fail — OpenAI function calling earned its reputation in the demo. Ten minutes of wiring gets one weather function returning perfect JSON arguments on every run. Since strict mode arrived, the arguments match ...
• Semantic chunking rarely beats fixed-size chunking — Every RAG tutorial walks the same road. Start with fixed-size chunking, label it naive, then graduate to semantic chunking as the adult choice. The reasoning feels airtight: rather than cutting a docum...
• Idempotency key: only useful if the server honors it — Search for idempotency key and every result tells you the same thing. Generate a unique string, attach it to a request header, and the server returns the saved response instead of running the operation...
• What happens when an agentic workflow runs — Every article about agentic workflows says the same thing: an AI agent makes a plan, uses tools, reflects on results, and keeps going until the task is done. That description is correct in the way that...
• Why automations break: one field rename, 72 hours of silence — Here is a story about why automations break that has nothing to do with bad logic, wrong credentials, or server outages. A mid-size SaaS company ran four Zapier workflows off the same HubSpot webhook....
• How to Build an MCP Server: 6 Decisions Every Tutorial Skips — Your first MCP server takes about 15 minutes. You install the SDK, define a tool with a decorator, run mcp dev server.py, and watch the Inspector spit back a result. Then you try to connect it to a rea...
• LLM fallback breaks at the prompt, not the network — Every LLM fallback tutorial follows the same pattern: request fails, retry with exponential backoff, switch to a backup provider, return the response. The retry logic gets pages of documentation. The H...
• n8n vs Make: What Happens When You Wire an LLM Into Your Automation — Every n8n vs Make comparison publishes the same table. Integrations: Make has 3,000+, n8n has 400+ (per their respective integration directories). Hosting: n8n self-hosts, Make is cloud-only. Pricing: ...
• LLM temperature: the softmax math nobody shows you — Every LLM temperature guide on the internet says the same thing. Set it to 0 for factual tasks, 0.7 for creative work, somewhere in between for everything else. This advice has been copied so many time...
• Fix broken LLM JSON output without retrying the API — Every developer building on LLM APIs hits this wall eventually. The model returns what looks like valid JSON, json.loads() throws a JSONDecodeError, and the pipeline stops. The instinct is to retry the...
• Prompt caching: why most API calls pay full price — OpenAI's documentation says prompt caching can cut input token costs by up to 90% and latency by up to 80%. Anthropic quotes similar numbers. These figures are real, measured, and documented. They are ...
• AI agent vs chatbot: the difference is one while loop — Every vendor article explaining the AI agent vs chatbot distinction draws the same picture. Chatbots respond. Agents act. Chatbots are reactive. Agents are proactive. These descriptions are accurate in...
• Function calling vs structured output: when to use which — Every guide to function calling vs structured output gives the same advice: use structured output for data extraction, function calling for actions. That framing is correct and also completely useless....
• RAG pipeline not working? Fix the generation, not retrieval — You checked the chunks. You logged the retrieval scores. Cosine similarity sits above 0.85, and the top-three results contain exactly the paragraph the user asked about. The answer is still wrong. This...
• OpenAI structured output Pydantic errors and fixes — The OpenAI structured output tutorial makes it look clean. Define a Pydantic model, pass it to client.beta.chat.completions.parse(), get typed data back. Then you try a model from your real codebase, t...
• AI agent infinite loop? Fix your tool responses first — An agent that calls the same search tool 47 times in 3 minutes is not broken. It is doing exactly what the conversation history tells it to do. The tool returned "found 12 results" each time, and the a...
• OpenAI batch API: the hidden costs of 50% savings — When teams first discover the OpenAI batch API, the pitch sounds simple: submit requests in a JSONL file, wait up to 24 hours, pay half the token price. For GPT-5.4, that means $1.25 per million input ...
• AI agent framework: what breaks after you choose one — You spent a week evaluating CrewAI against LangGraph against AutoGen. You read the comparison posts — all ten of them, all saying roughly the same thing. You ran the quickstart tutorials. You picked on...
• How to run an LLM locally: when it's actually worth it — Running an LLM locally is the developer equivalent of grinding your own coffee. Philosophically appealing and expensive in ways that don't show up on the invoice. The Ollama crowd will tell you that l...
• Prompt Injection Examples: 4 Real Attacks in Production — The Claude Cowork launch in January 2026 had a short honeymoon. Two days after release, researchers at PromptArmor uploaded a Word document with hidden white-on-white text and watched Claude obediently...
• LangSmith vs Langfuse: your stack decides it for you — Support ticket classifier in production. Three weeks live, accuracy has dropped from 94% to 71%. The engineer opens LangSmith, runs a trace session, spends 45 minutes identifying that the model is toke...
• Prompt caching fails when you mix static and dynamic content — Teams implement prompt caching, check the API pricing docs, and expect their Claude bills to drop by 90%. Then the billing statement arrives. The line items look exactly the same. This happens constan...
• MCP vs API: why MCP exists and when you still need APIs — A fintech team connected their AI agent to Stripe’s API last month. Stripe exposes over 300 endpoints. The agent needed to process a customer payment. It chose POST /v1/charges. That endpoint works. I...
• AI Code Agent 2026: What 80% SWE-bench Accuracy Hides — Every major AI code agent improved its scores in early 2026. Claude Code hit 80.9% on SWE-bench Verified. Cursor Composer 2 reached 73.7% on SWE-bench Multilingual. GitHub Copilot, despite not publishi...
• LangChain vs LangGraph: why chains ran out of room — Every langchain vs langgraph argument in production traces back to a specific failure mode. Here is one of them. A user asks a customer support bot about a disputed charge. The agent looks up the trans...
• No-code automation platforms still make you do the thinking — The no-code automation market hit $48.9 billion in 2026, according to Fortune Business Insights. Zapier alone connects over 8,000 apps. Make handles complex multi-branch logic through visual canvases. ...
• How to automate email responses with AI (2026 guide) — The average knowledge worker spends 28% of the workweek reading and responding to email, according to McKinsey's workplace productivity research. Microsoft's 2025 Work Trend Index puts the daily incomi...
• Make.com vs n8n: both assume you build it yourself — Every make.com vs n8n comparison frames the same decision: pick Make if you want a visual no-code interface, pick n8n if you need developer flexibility and self-hosting. That framing is accurate as far...
• OpenAI API rate limits: why retries won't save your workflow — You hit a rate limit error. You add exponential backoff. The error keeps happening. You increase the backoff ceiling. It still happens. You open a support ticket. This is the standard loop, and it exi...
• Anthropic prompt caching: what breaks in production — Anthropic's prompt caching documentation is accurate and technically complete. It's also silent on the failure modes that actually matter in production. The docs explain the mechanism correctly: mark ...
• Why AI agents fail in production (it's the abstraction) — The demo always works. You show the AI agent handling a support ticket. It reads the email, classifies the issue, drafts a response, checks the CRM, and closes the loop. Your stakeholders are impresse...
• Build an MCP server that works in production (not just demos) — Every MCP server tutorial covers the same ground: install the SDK, define a few tools, register with Claude Desktop, watch the demo work. That setup gets you to a working prototype in under an hour. Th...
• n8n vs Zapier: the AI workflow failure nobody talks about — Every n8n vs Zapier comparison runs the same playbook. Zapier costs more per task but requires zero maintenance. n8n costs almost nothing to self-host but requires a developer. The feature tables look ...
• LLM structured outputs: what they fix and what breaks next — Structured output mode (what OpenAI calls “strict mode,” what Anthropic implements through tool use schemas) works by constraining the model’s token generation at decode time. At every step, the decode...
• Webhook vs API: What Actually Breaks in Automations — Every article comparing webhook vs API gives you the same answer. Webhooks push data when an event occurs. APIs pull data on request. Accurate, and also the least useful thing you can know when you are...
• GPT-5.4 computer use: when the benchmark doesn’t matter — Everyone’s talking about the 75% OSWorld score. GPT-5.4 computer use just became the first AI model to beat human experts at autonomous desktop automation, and the coverage treats this like a signal to...
• AI agent observability: why your dashboard lies (2026) — Notion's engineering team spent two weeks tracking down a bug in their AI assistant that traditional monitoring never flagged. The agent was returning plausible, well-formatted answers to user question...
• Cut AI automation costs by 80%: a practitioner's guide — Most teams discover the same thing about three months into their AI automation rollout: the API bill is four to five times what they budgeted. A single workflow that processes customer support tickets ...
• Multi-agent orchestration: the one pattern that works — Every AI conference in 2026 features at least three talks about multi-agent orchestration. Swarms of specialized agents collaborating autonomously. Workflows that route tasks, delegate subtasks, and se...
• AI agent payments: what happens inside the auth chain — In early March 2026, Santander and Mastercard completed Europe's first live AI agent payment inside a regulated banking environment. An AI agent, operating with delegated authority from an account hold...
• Model context protocol security: the risks nobody discusses — Somewhere in the last six months, the model context protocol went from an interesting Anthropic side project to the connective tissue of every AI agent stack. The MCP registry hit 6,400 servers in Febr...
• How to run local AI models on your computer (2026 guide) — The default way to use AI in 2026 is still through a browser tab. Open ChatGPT, type a prompt, wait for a response. But there's a growing group of developers, privacy-conscious professionals, and tinke...
• Agentic AI for business: what operations teams need to know — Agentic AI for business is the biggest shift in operations technology since RPA. It's software that can reason through a problem, decide what to do next, and take action on its own. Unlike traditional ...
• AI automation vs RPA 2026: what operations teams need — RPA was supposed to fix operations. For a while, it did. Bots ran invoice processing, moved data between systems, filled out forms. The pitch was compelling: automate the repetitive stuff, free up the ...
• ChatGPT vs Claude vs Gemini 2026: honest comparison — The question comes up constantly. Someone's paying for ChatGPT Plus, they've heard Claude is better for writing, Gemini is cheaper, and now they're second-guessing everything. It's a fair question. The...
• Why AI Automations Fail in Production (And How to Fix It) — When Volkswagen launched Cariad in 2020, they had a vision: one unified AI-driven operating system for all 12 brands. By 2025, it had cost $7.5 billion in operating losses and delayed product launches ...
• Transform Your Automation Experience with Our Latest Updates — Say Goodbye to Friction in Your Automation Workflows Before, tracking the usage of your automations was a hassle. You had to guess how often they were being used, leading to inefficiencies. Now, with ...
• CCID: How Chase Agents Transformed City Research & Intelligence — The Problem The Cape Town Central City Improvement District (CCID) faced a classic problem: big-city work, small-team tools. Its Research & Economic Intelligence division struggled with fragmented, ma...
• HealthyMe Living: How Chase Agents Cut Automation Costs — "Chase Agents has opened up many new doors of how to interact with customer data. The options are endless and have helped me to complete tasks that I, as a solopreneur, would've had to pay lots of mone...
• How Chase Agents Solved AI Hallucination with Dual-Agents — I recently heard about a friend who built an automation in Make.com. It was supposed to automate order management for their Shopify store. For months it worked perfectly. Then one day it stopped workin...
• Easy Setup Guide for Chase Agents — Welcome to Chase Agents. This 3 minute guide will walk you through the essentials to get up and running in about 15 minutes. Step 1: Create Your Free Account Go to https://chaseagents.com and sign up...
• Make AI Automation Actually Work: A Guide to Chase Agents — Sarah was spending 3 hours every morning manually checking 15 different websites for new client leads. Not fun. Not scalable. As the owner of a small marketing agency, she was technically savvy but not...
• AI Model Selection Guide for Chase Agents — Chase Agents is a chat platform that uses AI to automate your work. You can automate a wide variety of tasks through different connections, APIs, and MCPs. The better the model you use, the better it c...
• AI Art Director for Marketing Videos | Chase Agents — Let me be honest. I'm a builder, not a marketer. When I try to make video ads, they come out looking like a developer made them. Because a developer did. And it shows. For every product I've shipped, ...
• Introducing Chase Agents: Build AI-Native Automations Without Code — Introducing Chase Agents: Build AI-Native Automations Without Touching a Single Line of Code I just spent the last few months building something I genuinely believe will change how teams automate thei...