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Customer stories
How teams run their work on Major.
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Harris Technology ServicesHow Harris Technology Services (HTS) Runs their Operations on Major
At HTS, Major is the second brain that the entire team uses to make operational decisions quickly. They can ask questions against their data, build dashboards, generate statements of work all on the fly with Major.
ComunHow Comun’s CX team built a Voice of the Customer Dashboard in One Week - Without a Single Engineer
At Comun’s 2026 hackathon, 2 CX leads and a UX researcher built a production ready voice of the customer application with no engineering support.
RillaHow Rilla supported a 2000 person conference with Major
Leading Voice AI company Rilla used Major to quickly build apps to support their 2000 person annual conference. Their teams built and deployed Major apps within 15 minutes of an issue being reported by their conference staff.
Technical
How we build Major: identity, infrastructure, models, and the tradeoffs behind them.
Opus 5.5 is insane
Opus 5.5 cut the cost of a Major agent session by 79% compared to Opus 5.0. Here's where the savings come from and why cheaper models are good for a hosting platform.
A better Vercel for AI generated internal apps
Vercel was built for consumer web apps, not your team's internal dashboard. Why teams host AI-generated apps, agents, and workflows on Major instead.
Identity in AI generated apps, agents and workflows
How Major handles identity and authorization for AI-generated apps, agents, and workflows: scoped deployment identities, a connector proxy, live revokes, and the tradeoffs.
Model routing is now supported on Major. Here's how we did it.
How we added multi-model support to an agent built on the Claude Agent SDK: a model gateway, deferred tools for any provider, OTEL-based evaluation, and a cost vs. intelligence picker.
Blog
Notes from the team on building, deploying, and operating internal apps with Major.
AI Agent Memory: Durable State Beyond the Context Window
An agent’s context window is not durable memory. See how databases, retrieval, and deterministic apps let work persist across runs, with governance built into execution.
Agentic AI Governance: Put Control Where Agents Act
Agentic AI governance is not a policy document around a model. It is the authority, controls, and evidence built into the systems where agents do work.
AI Agent Access Control: Scope Permissions Where Agents Act
An agent’s access policy is only useful if it travels with each action. Design access around scoped identities, explicit authority, and auditable app execution.
Agent Observability: What Deterministic Apps Change
Tracing model calls matters, but it is only part of agent observability. See how deterministic apps make repeated execution inspectable and change what teams need to monitor.
Enterprise AI Strategy: Build for Repeatable, Governed Work
An enterprise AI strategy needs more than model access and isolated pilots. The durable path is to decide where judgment belongs, move repeatable work into governed software, and make every action inspectable.
Enterprise AI Platforms: A Buyer’s Guide to Governed Agents
Enterprise AI platforms differ in what happens after a model makes a plan. Compare them by the production criteria that matter: deterministic execution, durable state, permissions, auditability, and how much work still depends on fresh reasoning.
AI Agents for Project Management: Use Cases and Governance
Learn how AI agents support project management through planning, coordination, reporting, and risk workflows, with the state, permissions, and human oversight needed to use them responsibly.
Kimi K2 vs Claude: What Can Enterprise Agents Safely Delegate?
Kimi K2 and Claude represent different deployment choices. Compare open-weight control with managed frontier reliability through the question enterprise teams actually face: what can an agent safely delegate?
Cheapest LLM API: Why the Lowest Token Price Rarely Wins
The cheapest LLM API is rarely the cheapest system. Compare token price with retries, routing, failure cost, and the deterministic app layer that makes spend predictable.
GitHub Automation: From Actions to an Engineering-Ops Agent
GitHub Actions is right for CI. Cross-system engineering operations need a predictable agent that connects GitHub, Linear, and Slack, then runs repeatable triage as a governed app.
How to Build a Claude Agent: Tools, State, and Workflows
Learn how to build a Claude agent that turns repeatable reasoning, tool calls, and multi-step workflows into a dependable app with state, guardrails, and governed access.
Enterprise Chatbot Platforms: When Conversation Needs an App Layer
Enterprise chatbot platforms answer questions, but production work often needs more: durable state, permissions, audit, and an app layer that executes repeatable steps.
Replit Alternative: When You Need Governed Apps, Not Just a Cloud IDE
Looking for a Replit alternative? Compare tools by what they ship and how they run. Major leads when agents need to build governed, stateful apps for repeatable enterprise work.
AI Agent Builders: From Model Calls to Deterministic Apps
An AI agent builder should do more than connect prompts to tools. The production question is whether repeatable work becomes governed software with state, permissions, and logs.
Enterprise AI Platforms: What Makes One Production-Ready
Enterprise AI platforms need more than model access. The durable test is whether they ship governed apps with state, permissions, audit, and predictable execution.
AI Workflow Automation: From Repeated Prompts to Governed Apps
AI workflow automation is most useful when judgment stays with the model and repeatable work moves into deterministic, governed apps that keep state across runs.
Building AI Agents: A Practical Guide to Production-Ready Systems
Building an AI agent is easy when the goal is a demo. Production requires a bounded task, scoped tools, durable state, approvals, observability, and a plan for moving repeatable work into deterministic software.
AI Workflow Automation Tools: A Governed Buyer’s Guide
A buyer-oriented brief for evaluating AI workflow automation tools through production needs: bounded credentials, approvals, durable state, observable runs, audit evidence, and deterministic application controls.
OutSystems vs Lovable vs Bolt AI: Enterprise Comparison
Compare OutSystems, Lovable, and Bolt AI through enterprise build workflows, state, governance, and repeatable execution, with Major leading the evaluation framework.
AI Workflow Builder: Build Workflows That Keep Running
An AI workflow builder can turn a plain-language process into working automation. The production challenge is designing state, retries, approvals, observability, and permissions before moving repeatable steps into deterministic apps.
What Is a Low-Code Automation Platform?
A low-code automation platform helps teams turn repeatable workflows into software with visual tools and code when needed. Learn how to evaluate automation for governed enterprise execution instead of isolated recipes.
Replit vs Lovable: A Comparison for Enterprise Teams
Compare Replit and Lovable for teams evaluating prompt-led software creation, then assess the operational requirements that matter when a workflow must persist, run predictably, and remain governed.
LLM Cost Management: Control Spend by Running Less AI
LLM cost management starts with visibility, budgets, routing, and attribution. The durable fix is architectural: move repeatable work into deterministic apps so token demand grows more slowly as usage grows.
AI Model Selection: Choose Per Task, Not by Hype
Choosing an AI model is a workload decision, not a leaderboard decision. Use a scorecard for quality, latency, cost, reliability, and governance, then keep the architecture reversible.
LLM Routing: How It Works and What the Benchmarks Show
LLM routing sends each request to the model best suited to it. The technique is real and the savings can be real, but the newest benchmark finds several commercial routers fail to beat a simple baseline. Here is how routing works and where it stops helping.
Self-Hosted LLM: What It Costs and When It Pays Off
Every self-hosted LLM guide reprints the same breakeven number, and their own cost tables contradict it. Here is the honest math on GPUs, throughput, and the engineer time nobody counts, plus the cheaper lever almost nobody pulls.
Multi-Agent Orchestration: Patterns and What They Cost
Every ranking guide on multi-agent orchestration explains the patterns and stops there. None of them asks what it costs to have a model re-derive the same routing decision on every single run. Here are the patterns, and the point where coordination should stop being a reasoning p
AI Model Orchestration: Routing Models vs Orchestrating Work
Most pages using the phrase mean three different things at once. Model orchestration routes a request to the right model. Agent orchestration coordinates multi-step work. Conflating them is why teams buy a router when they needed an architecture.
LLM Observability: What to Instrument When the Work Is a Black Box
Tracing a model call tells you what the model did, not why the work was right. The vendors selling LLM observability skip the standard that makes traces portable, and skip the harder question: how much of your system needs watching in the first place.
Claude Code Alternatives: 12 Coding Agents Compared (2026)
Claude Code is not the only agent that can write your code. Twelve alternatives compared on surface, model choice, and what you actually pay, plus the option that ships your work as a governed app instead of a file.
Gemini vs ChatGPT: How to Choose Without Locking In
Every comparison answers the question a consumer picking a $20 subscription would ask. If you are building software on one of these models, the useful question is different: what does it cost to change your mind in six months, and how do you design so that answer stays cheap?
Best Coding LLM: Choose by Task, Then Govern the Workflow
The best coding LLM depends on the task, constraints, and workflow around it. Compare coding strengths, then decide what should run as deterministic software instead of being re-reasoned.
DeepSeek API Pricing: What You Actually Pay Per Run
Most DeepSeek pricing pages quote a single input number. The official docs quote six, split by peak hour and cache hit. Here is what the rate card actually says, why the aggregators disagree with it, and why per-token price is the wrong thing to optimise anyway.
Qwen API Pricing: Models, Regions, and Real Usage Cost
Qwen API pricing varies by model, region, token direction, caching, and batch mode. Use a normalized framework to compare published rates with the cost of running a real workflow.
LLM API Pricing in 2026: The Rate Card Is Not the Bill
Every LLM pricing table shows two columns: input and output. Your invoice has a dozen more, cached reads, cache writes, context tiers, batch discounts, service tiers, tokenizer density. Here is what the rate cards actually charge you for.
Gemini API Pricing: Every Tier That Changes What You Pay
Google's pricing page runs 9,500 words across three dozen near-identical tables and answers every question except the one you came with. Here is the same rate card compressed: which model, which tier, what the 200k threshold does, and what changes on January 1.
Grok Pricing in 2026: Plans, API Costs, and Agent Spend
Every Grok pricing page lists the subscription tiers and the per-token rate. None of them explains the 200k context cliff that reprices your whole request, or what a recurring agent workflow actually costs on its thousandth run. Here is the rate card, and the part that comes afte
Claude vs GPT: Which Model for Which Job in 2026
The flagship models are close enough that raw capability is no longer the deciding factor. Here's what separates Claude and GPT on cost, context, agent tooling and ecosystem, and how to choose without betting the architecture on it.
Best LLM for Coding: How to Pick Per Task in 2026
Every "best LLM for coding" list crowns one winner that goes stale within a quarter. Here is the per-task way to choose instead: which models suit long-context refactors, greenfield builds, debugging, and code review, and how to keep the choice reversible.
Salesforce Automation: From Flow Rules to an Agent That Reads the Pipe
Flow handles the rules you can write down. The work that needs judgment across accounts, calls, and contracts still lands on a human. Here is what the Salesforce API actually gives an external agent, the governor limits you trade for, and a worked pipeline-hygiene build.
Google Sheets Automation: Surviving the Second Week
Macros, Apps Script, and Zapier all get a sheet automation working on day one. None of the guides covers day eight, when a re-run duplicates every row it already processed and nobody finds out for a week. Here is what a Sheets automation needs to keep running.
Major x Snitchfeed: Official API and MCP Connectors for Social Listening
Snitchfeed's official API and MCP connectors are live on Major. Run keywords through an SEO app into a live CMS post, turn signals into LinkedIn posts via Taplio, draft Reddit AEO replies for a person to post, and listen to named accounts on LinkedIn and X.
Mistral API Pricing: Rates, Modes, and Real Cost
Mistral API pricing spans models, standard, batch, priority, cached input, and regional inference. Learn how to read the rate card and estimate the cost of a real workflow.
AI Agent Cost: What It Takes to Build One and What It Costs to Run
Every guide to AI agent cost quotes a build range between $10,000 and $500,000 and cites nothing. The number that actually matters is what one run costs and how that scales. Here is the arithmetic, from published rate cards.
How to Build AI Agents That Survive Run 1,000
Every guide teaches you to get one run working. None covers what breaks on run 1,000: vanishing state, climbing token spend, unscoped credentials, and no record of what the agent did. Here is the build, through to production.
LLM Evaluation: Methods, Judge Bias, and Shrinking the Surface
Every guide to LLM evaluation recommends using a model as a judge. Almost none of them cite the research on how those judges fail. Here is what the papers actually found, and why the size of your evaluation problem is an architectural choice.
AI Agent Architecture: Components, Patterns, and the App Layer
Most agent architecture diagrams show components that reason on every single run. That is one layer, not the whole picture. The missing one is where work stops being reasoning and becomes software the agent calls instead.
Anthropic vs OpenAI: An API Comparison for Builders
Most Anthropic vs OpenAI pieces cover the rivalry. This one covers the APIs: auth headers, batch discounts, rate-limit models, the pagination trap, and why the right answer is usually to run both behind one governed workflow.
Model Routing: How to Send Each Request to the Right Model
Routing sends each request to the model that fits the job instead of billing every task at frontier rates. Here are the strategies that work, how to tell whether yours is helping, and the one route most teams never configure: the step that no longer needs a model at all.
DeepSeek vs ChatGPT: The Cost Gap and What to Move
Which chatbot writes a nicer poem is the wrong question. Which workloads can you move to a cheaper model, and how do you swap models later without rewriting the workflow?
Multi-Agent AI: What It Costs and When It's Worth It
Every explainer tells you multi-agent systems are more scalable. Anthropic's own engineering data says they burn about 15x the tokens of a chat. Here's the arithmetic nobody publishes, and the cheaper pattern most teams should reach for first.
Notion Automation: Where Built-In Rules Stop and Agents Start
Notion's built-in automations fire inside one database and stop there. This is the honest map of what they do, what the API adds (webhooks included, at 3 requests per second), and how to build an agent that reads Notion for context but keeps its state somewhere durable.
AI Model Governance Can't See What Your Agent Did
Model cards, drift charts, and validation gates were built for models that output numbers. They govern the artifact, not the action. When an agent calls a tool with your credentials, the entire model-governance vocabulary has nothing to say about it.
AI Governance Framework: One You Implement, Not Attest To
Most AI governance frameworks are documents. They name a committee, list principles, and stop exactly where enforcement begins. Here is a framework mapped to NIST AI RMF and ISO/IEC 42001 that specifies the control, the owner, and the artifact for each function.
LLM Gateway: What It Governs, and What It Cannot
Every LLM gateway page sells governance and none of them specify it. Here is what a gateway actually controls, the RBAC and credential-scoping questions to ask before you buy, and the class of risk that no gateway can see.
Kimi vs Claude: Open Weights, Real Costs, Current Models
Every Kimi vs Claude comparison on page one benchmarks models that are now a generation old, and none of them cite a single benchmark source. Here is the current-generation comparison, priced properly - cache reads included.
AI Agent API: What Persists After the Request Ends
"AI agent API" means two opposite things, and the SERP serves both without noticing. Here is the precise definition, the four vendor approaches compared, and the design decision nobody covers: what actually survives after the request returns.
AI Coding Agent: What Happens After the Code Is Written
Every AI coding agent roundup scores the same things: context window, multi-file edits, memory, price. Not one scores what happens after the code exists. That gap is where the real work lives: review, deployment, permissions, audit.
Grok vs Gemini: Pricing, Context, and Which One to Wire In
Every Grok vs Gemini comparison on page one is a consumer subscription review. None of them price the API, and they contradict each other on the numbers. Here is the developer-grade comparison , plus why the answer should stay cheap to change.
Replit vs Lovable (2026): The Verdict and What It Actually Costs
Replit and Lovable both get you a working app fast. The difference shows up on the bill, on which one a beginner can actually finish, and on day 30 when the prototype needs auth, audit and durable state. Here is the verdict and the third option neither tool offers.
Claude AI Agents: What to Build So You Stop Re-Prompting
Everyone shows you how to make Claude do a task. The harder question is what Claude should build so the task stops needing a model. A practical look at the Anthropic API, its real gotchas, and a contract-triage agent worth shipping.
LLM Orchestration: The Layer That Should Use Fewer Model Calls
Most orchestration guides teach you to chain more model calls together. That is the expensive path. Here's what LLM orchestration actually is, the patterns worth knowing, and why the best orchestration layer keeps shrinking the work the model does.
Grok vs Claude: How to Choose (and Why You Shouldn't Commit)
Grok is cheaper and sees X in real time. Claude reasons deeper over long context. Here's what the numbers actually show, where each one earns its price, and why routing per task beats picking a side.
Best Vibe Coding Tools in 2026: A Builder's Guide to Picking One
Every vibe coding roundup ranks tools by how fast they get you to something that works. That's the right test for a prototype and the wrong one for an app other people will use. Here's the split that matters, and what happens after it works.
OpenRouter Alternatives: 10 AI Gateways and Routers Compared
LiteLLM, Portkey, Requesty, Bedrock and more, compared on pricing, self-hosting and governance. Plus the harder question: does your workload need a router, or an app that stops re-reasoning?
LLM Cost Optimization: What Works and What It Misses
Caching, routing, and batch APIs cut the price of a model call. Here is which lever fits which symptom, and why removing repeatable calls beats discounting them.
Claude Agent: What It Is and How to Build One That Lasts
Claude agent means four different things. Here is the one that matters if you are building, plus the API details and the gotchas nobody documents.
AI Agents for Healthcare: Back-Office Workflows You Can Govern
Prior authorization, referrals, claims follow-up, intake, scheduling, and coding drafts are the healthcare workflows agents can carry today. What decides whether they are safe to run is governance: PHI scoped to the minimum, every action logged, a human on consequential calls.
Agent Platform: What It Means Now That Google Owns the Term
Google renamed Vertex AI to Gemini Enterprise Agent Platform and now owns most of this search result. That makes "agent platform" a product name and a category at once. Here is the neutral definition, the criteria that decide the choice, and where each camp fits.
AI Orchestration: Coordinating Models, Agents, and the App Layer
Most orchestration guides treat "the model" as one abstraction and never name a single one. Real systems route across several, by task and by cost. Here is how model orchestration, agent orchestration, and the app layer actually divide the work.
LLM Router: Routing Strategies and Their Real Limits
An LLM router picks which model answers each request. The four routing strategies that work, how routers differ from gateways, and what routing cannot fix.
Grok vs ChatGPT: Key Differences and a Reversible Choice
Grok and ChatGPT differ in real ways: context windows, API pricing, coding, live X data. Here is the honest comparison, and why the choice isn't permanent.
Asana Automation: From Rules to an Agent That Reads Across Projects
Asana Rules are great inside one project. This is what comes next: an agent that reads across projects, flags slipping dependencies, and drafts a weekly exec status digest for you to approve.
AI Agents for Project Management: 6 Cross-Tool Workflows That Actually Help
Every PM tool ships its own AI, but real programs span Jira, Asana, Linear, and Monday. Six cross-tool agent workflows, status rollups, risk detection, and exec digests, that keep the PM in control.
Airtable Automation: From Trigger-Action Rules to a Record-Enrichment Agent
Airtable's automations are fine for simple in-base rules. This is what comes next: an agent that enriches new records, validates them against your rules, and routes the exceptions to a human.
Slack Automation: From Workflow Builder to an Alert-Triage Agent
Slack Workflow Builder is capable now. This is what comes next: an agent that reads an alert, pulls context from Linear and GitHub, and posts a structured Slack message with action buttons for a human.
Gmail Automation: From Filters to an Inbox-Triage Agent
Filters and templates sort your mail, but they can't read it. Here's the next step: an inbox-triage agent that classifies email, drafts replies for one-click approval, and captures leads to your CRM, with guardrails.
Zendesk AI Agents: How to Build a Triage-and-Draft Agent You Control
Zendesk sells autonomous AI agents priced per resolution. Here is the other option: build a triage-and-draft agent on the Zendesk API that reads a ticket, checks your knowledge base, and drafts a reply for human approval.
Shopify Automation: From Flow Recipes to a Cross-System Agent
Where Shopify Flow fits, when an AI agent on the Admin API earns its place, and how to build a governable low-stock reorder agent.
Bolt.new Alternatives: 8 AI App Builders Ranked (2026)
Eight real Bolt.new alternatives ranked by how well each carries a prototype into production, from Major for governed stateful apps to Lovable, Replit, v0, Base44 and Bubble.
AI Agents for Accounting: 6 Close & Bookkeeping Workflows (Done Auditably)
AI agents can reconcile, match invoices, and draft close commentary. The question accountants actually ask is whether they are auditable. Here are 6 workflows that keep a human in control and every action logged.
Enterprise AI Governance: From Policy to Implementation
Enterprise AI governance is the set of controls that keep AI systems accountable in production. For autonomous agents, it means governing the actions an agent takes, not just outputs, enforced where the action happens rather than in a policy document that nothing checks.
AI Agent Security: The Threat Model and How to Contain It
Prompt injection has no reliable fix. AI agent security is about bounding what a compromised agent can reach, mapping each OWASP risk to a containing control.
Workato Alternatives: 8 Automation and AI-Agent Tools Compared (2026)
An honest comparison of 8 Workato alternatives, Make, n8n, Zapier, Tray, Boomi, Celigo, and Pipedream, plus where an agent that builds governed, deterministic apps fits when recipes hit their limits.
Retool Alternatives: 9 Internal-Tool Builders (and One Different Appro
Ten Retool alternatives compared: Major, Appsmith, Budibase, ToolJet, Superblocks, UI Bakery, DronaHQ, WeWeb, Power Apps and Jet Admin, ranked by how much work each leaves on your team.
AI Agent Use Cases: A Predictable Pattern for Enterprise Work
Most AI agent use case lists are industry catalogs. Organize them around four repeatable patterns, reconcile, triage, route and draft-for-approval, and map each to a governed app.
Make.com Alternatives: 8 Automation Tools Compared (2026)
Eight Make.com alternatives ranked for governed production: Major, n8n, Zapier, Workato, Tray, Power Automate, Lindy, Gumloop and Activepieces.
AI Agents for Ecommerce: 6 Operational Workflows Beyond the Chatbot
Most AI agent guides for ecommerce rank support chatbots. Here are six operational workflows across Shopify, Stripe, and Slack, and how to build the first one on Major.
AI Agents vs. RPA: The Real Difference (and Why It's Not Either/Or)
RPA is reliable but brittle, AI agents are adaptive but non-deterministic. The real answer is not picking one, it is an agent that reasons about the exception, then commits the repeatable path to a deterministic app.
AI Agent Governance: Why Control Must Move to the Point of Action
AI agent governance is the set of controls that determine what agents can do, what data they can act on, and who is accountable when they get it wrong. Here is how to build agent-level governance that scales.
Lovable Alternatives: 10 AI App Builders Compared (2026)
Lovable ships a demo in minutes, but its output is a starting point, not a production app. We compare 10 alternatives across prompt-to-app, owned-code, and governed-production platforms, with honest fit for each goal.
Replit Alternatives: 10 AI App Builders and IDEs Compared
Replit is great for prototyping, but teams outgrow it once an app needs real data, auth and audit. Compare 10 alternatives, with Major first for governed apps and agents.
n8n vs Zapier: Pricing, Self-Hosting, and When to Use Each
n8n bills per execution and self-hosts free. Zapier bills per task with 9,000-plus integrations and zero setup. Pricing, self-hosting, and fit, plus where Major fits when workflows must decide.
Zapier Alternatives: 10 Automation Tools and AI Agents Compared
Zapier bills per task and chains fixed triggers. We compare 10 alternatives across visual automation, self-hosted, and AI-agent platforms, with Major first for work that has to become governed software.
n8n Alternatives: 10 Workflow and AI Agent Tools Compared
Outgrowing n8n self-hosting overhead or licensing? We compare 10 alternatives across open-source, cloud, and AI-agent platforms, with Major first for governed production work.
Natural Language to SQL: Building a Guarded Postgres Agent
Natural-language-to-SQL tools are accurate enough to be useful and wrong often enough to be dangerous. Here is how to build a Postgres query agent with read-only access, blocked writes, and row caps.
AI Customer Service Agents: Use Cases and How to Build One
What an AI customer service agent does, the seven support workflows teams ship first, and how to build a governed triage and draft agent on Major across Zendesk, Postgres, Notion and Slack.
Enterprise AI Agents: What "Enterprise-Grade" Actually Means
Enterprise-grade means deterministic execution, durable state, scoped credentials and audit trails at the point of action. Here is how to evaluate an agent and build one on Major.
n8n vs Make: Which Workflow Platform Wins?
Compare n8n and Make on hosting, metering, integrations and AI, then see where Major's deterministic app layer fits when a node graph is no longer enough.
How to Build an Agentic Workflow: Patterns, Steps, and Limits
Most guides define agentic workflows and stop. This one builds one: the five patterns worth knowing, an ordered path from goal to running artifact, and the honest rule for deciding which steps should stop being reasoning and become code.
AI Agents for Marketing: Use Cases, Reporting, and Campaign Ops
Marketing teams use AI agents to automate campaign reporting, flag performance anomalies, and route leads across ad platforms, CRMs, and Sheets. See where they fit and how to build a governed, reusable agent.
AI Agents for HR: 7 Use Cases That Actually Ship
AI agents for HR go beyond chatbots to execute onboarding, offboarding, and policy workflows across your HRIS, Slack, and docs. Learn the concrete use cases, where to start, and how to build a governed HR agent that reasons once and runs forever.
Zapier vs Make: Which Automation Platform Wins in 2026?
Zapier vs Make: Zapier wins on ease of use and app breadth; Make wins on visual complexity and cost at scale. But the real 2026 question is whether rule-based automation is enough, or if you need an agent.
Jira Automation for Engineering-Ops AI Agents
Turn Jira from a ticket tracker into the command center for an engineering-ops agent. Learn how agents triage issues, link incidents to postmortems, and draft sprint analytics by reasoning across Jira, GitHub, Slack, Confluence, and PagerDuty.
10 Best Base44 Alternatives for Production Apps (2026)
Base44 is fast for prototypes, but its proprietary backend and credit limits push teams toward alternatives. Compare Major, Lovable, Bolt.new, Replit and more for governance, code ownership and production scale.
AI Agent Frameworks: An Honest Map for Teams Choosing a Layer
AI agent frameworks like LangChain and CrewAI build agent logic. See what each one gives you, what you still own, and when the platform layer is the real choice.
Best AI Agent Builder: How to Choose for 2026
A buyer's guide to the best AI agent builders in 2026, comparing Lindy, n8n, Zapier, Gumloop, Relevance AI, and Major on determinism, governance, and integration depth.
What Is Agentic Automation? A Practical Enterprise Guide
Agentic automation uses AI agents to plan and execute multi-step work. See how it differs from RPA, what it looks like on invoice intake, and how to build it so cost stays flat.
Lovable vs Bolt: AI App Builder Comparison for 2026
Lovable and Bolt.new both turn prompts into working apps, but one is design-first for non-coders and the other is code-first for developers. This brief maps the real trade-offs in speed, UI quality, backend maturity, and pricing, then shows where governed, stateful agents fit.
AI Agent vs AI Assistant: The Practical Difference
AI assistants answer when asked; AI agents finish the work across tools and time. Learn the practical distinction, when to use each, and how Major turns agentic ideas into deterministic apps.
AI Agents for Finance: Use Cases for FP&A and RevOps
Finance teams are moving from chatbots to autonomous agents that reconcile systems, draft commentary, and flag variances. This brief maps the top use cases, contrasts Stack AI, Ramp, and Datarails, and specifies a close-acceleration agent built on QuickBooks, Stripe, and Salesfor
AI Agents for Recruiting: 7 Workflows That Actually Speed Up Hiring
AI agents can source, screen, and schedule candidates while keeping recruiters in control. Learn the top recruiting workflows and how to build one that writes back to your ATS.
AI Agents for Insurance: Claims, Underwriting & Governance
See how insurance teams use AI agents for claims intake, submission triage, and policy Q&A, and why governance, auditability, and deterministic execution matter most in regulated carriers.
What Is Vibe Coding? From Front-End Demos to Production Agents
Vibe coding turned "describe it and ship it" into a real workflow, but most vibe-coded apps never reach safe production. Here is what vibe coding actually is, where it breaks, and how the same idea extends to building AI agents on real enterprise data.
AI Agent Observability Isn't a LangSmith Feature
LLM observability tools don't observe agents. They observe tokens. Here's what real agent observability requires, and the questions to ask any vendor before you ship agents into production.
How to Create an AI Agent: A Build Guide That Survives Day Two
Creating an AI agent takes an afternoon. Getting it to do the same thing twice is the part nobody writes about. Here is the build procedure, the no-code and free options, and the step where most agents quietly start costing more than they save.
What Is an AI Agent? Definition, Types, and How to Build One
A precise definition of an AI agent, the five classic types, how the loop works, and what separates a production agent from a demo: reasoning you can audit and actions that run as code.
Salesforce AI Agents: 5 Workflows Beyond Agentforce
Agentforce isn't the only way to put an AI agent on Salesforce. Here's how to build one over the REST API, joining pipeline, Gong calls, and Slack to flag at-risk deals and draft follow-ups, with a human approving every send.
AI Agents for HubSpot: 5 Workflows That Actually Earn Their Keep
Five AI agents for HubSpot that actually earn their keep, built around deal-stage tracking, post-call CRM updates, and at-risk-deal Slack digests. Plus a worked example you can fork.
AI Agents for Notion: 5 Workflows That Use Notion as the Data Layer
Five AI agents for Notion that use it as the data layer, not the surface. Roadmap digests, knowledge-base Q&A, meeting-note-to-task agents, and how to build them on Major.