# Aldena > Aldena is a workplace simulator where founders and operators orchestrate AI agent teams working in a room you can watch. ## Pages - [Home](https://aldena.ai): product landing page - [Features](https://aldena.ai/features): how rooms, agents, memory, org charts, and skills work - [Integrations](https://aldena.ai/integrations): connectors and technologies agents work with, one page per tool - [Agents](https://aldena.ai/agent): the built-in agents you can hire, one page per role - [Pricing](https://aldena.ai/pricing): team plan prices, usage credit rates, and what every plan includes - [Models](https://aldena.ai/models): supported AI models - [Image model pricing](https://aldena.ai/models/image): image generation model rates - [Speech model pricing](https://aldena.ai/models/speech): speech synthesis model rates - [Transcript model pricing](https://aldena.ai/models/transcript): audio transcription model rates - [Video model pricing](https://aldena.ai/models/video): video generation model rates - [LLM leaderboard](https://aldena.ai/llm-leaderboard): every legally usable llm benchmark, aggregated into one honest ranking - [Best LLM for coding](https://aldena.ai/best-llm-for-coding): models ranked on issue resolution, webdev preference and security review - [Best LLM for agents](https://aldena.ai/best-llm-for-agents): models ranked on agentic tool use, steerability and recovery - [Best LLM for math](https://aldena.ai/best-llm-for-math): models ranked on research-grade problem sets and competition math - [Best LLM for vision](https://aldena.ai/best-llm-for-vision): models ranked on ocr, diagrams, homework and captioning - [Best AI for design](https://aldena.ai/best-ai-for-design): models ranked on producing interfaces people prefer - [Best AI for image generation](https://aldena.ai/best-ai-for-image-generation): image models ranked on generation and editing preference - [Best AI for video generation](https://aldena.ai/best-ai-for-video-generation): video models ranked on text to video, image to video and editing - [Best AI for text to speech](https://aldena.ai/best-ai-for-text-to-speech): speech models ranked on blind listening votes - [Best AI for transcription](https://aldena.ai/best-ai-for-transcription): speech recognition ranked on word error rate and speed - [Blog](https://aldena.ai/blog): articles, comparisons and guides - [Learn](https://aldena.ai/learn): answers to the questions people ask about ai agents and ai teams - [Changelog](https://aldena.ai/changelog): product release notes - [Contact](https://aldena.ai/contact): support, sales, privacy, and legal contact addresses - [Privacy](https://aldena.ai/privacy): privacy policy - [Terms](https://aldena.ai/terms): terms of service - [Log in](https://aldena.ai/login): sign in to an existing account - [Sign up](https://aldena.ai/signup): create a new account ## Feature pages - [isolated rooms: sandboxed ai agent workspaces with their own server](https://aldena.ai/features/isolated-rooms): every aldena room is an isolated environment with its own server, integrations, credentials, and memory. nothing crosses between rooms, so work stays put. - [ai agent memory: private per-agent notes plus shared room context](https://aldena.ai/features/agent-memory): aldena gives every agent private long-lived memory and every room a shared context all its agents read. both survive the chat that created them. - [ai agent org chart: managers, reports, and delegation you can watch](https://aldena.ai/features/org-chart): wire agents into a real hierarchy on a live canvas. work travels down manager lines to direct reports and results come back up, with no orchestration code. - [ai agent skills: attach pre-built and custom abilities to any agent](https://aldena.ai/features/skill-marketplace): the skill marketplace gives an agent a specific ability: how to use what is installed on the room server, or a procedure you wrote. every skill is versioned. - [ai agent roster: eleven prebuilt roles you hire into a room](https://aldena.ai/features/agents): aldena ships named agents for the roles a delivery team needs, from analyst to reviewer to bug fixer. hire the ones you want and pick each one's model. - [human in the loop ai agents: approval gates and review](https://aldena.ai/features/human-in-the-loop): every tool an agent can reach runs under an ask, allow, or deny policy. agents stop mid-run to ask you, and code lands as a pull request they never merge. - [mcp servers: connect any remote server, gate every tool](https://aldena.ai/features/mcp-servers): install a known mcp server from the marketplace in one click, or add any other with a name and a url. its tools reach your agents, allow, ask or deny per tool. ## Integrations - [bitbucket ai agent: pull requests, pipelines, and deployments](https://aldena.ai/integrations/bitbucket): connect bitbucket to a room and its agents clone repositories to the room server, open and review pull requests, run pipelines, and manage deployments. - [github ai agent: branch, push, and open the pull request](https://aldena.ai/integrations/github): connect github to a room and its agents sync the repo to the room server, open branches, push commits, raise pull requests, and write reviews on approval. - [ai agent for gmail: draft, label, and send on approval](https://aldena.ai/integrations/gmail): connect gmail to a room and its agents search threads with gmail query syntax, read messages, draft replies, manage labels, and send only on an opt-in scope. - [ai agent for google drive: search the files, read them, work from them](https://aldena.ai/integrations/google-drive): connect google drive to a room and its agents search and list files, read content, check sharing permissions, and create or copy files under approval. - [ai agent for jira: pick up the issue and do the work behind it](https://aldena.ai/integrations/jira): connect jira to a room and its agents search with jql, create and edit issues, move them through the workflow, comment, log work, and build the change. - [ai agent for linear: read the issue, ship it, move the state](https://aldena.ai/integrations/linear): connect linear to a room and its agents search issues, create and update them, change workflow state, comment, relate issues, and build the change described. - [ai agent for notion: query the database, write the page back](https://aldena.ai/integrations/notion): connect notion to a room and its agents search the workspace, read pages as markdown, query data sources, create and update pages, and post comments. - [ai agent for sentry: read the error, find the cause, ship the fix](https://aldena.ai/integrations/sentry): connect sentry to a room and its agents search issues and events, read stack traces, replays and profiles, resolve or assign, and fix the code the trace names. - [ai agent for slack: read the channel, answer in the thread](https://aldena.ai/integrations/slack): connect slack to a room and its agents read channels and threads, search the workspace, post and schedule messages, react, and wake on the events you choose. - [ai agent for vercel: read the build log, deploy again](https://aldena.ai/integrations/vercel): connect vercel to a room and its agents list projects and deployments, read build logs down to the error, subscribe to events, and deploy on an opt-in scope. - [node.js ai agent: agents that install, build, and run your js app](https://aldena.ai/integrations/nodejs): install node.js on a room's server and its agents get node, npm and npx through the bash tool, so they install dependencies, run scripts, and start the app. - [playwright ai agent: run the suite, read the real failure](https://aldena.ai/integrations/playwright): install playwright with its browser binaries on a room's server and its agents run end to end tests headless, read the real failure, and run the suite again. - [pnpm ai agent: agents that install and run scripts in your workspace](https://aldena.ai/integrations/pnpm): install pnpm on a room's server and its agents install from the lockfile, run workspace scripts, and target one package with pnpm filter instead of everything. - [turborepo ai agent: agents that run the right tasks in a monorepo](https://aldena.ai/integrations/turborepo): install turborepo on a room's server and its agents run turbo tasks with the repo's own pipeline, use the cache, and rebuild only what the change touched. - [git ai agent: branch, commit, and rebase a real checkout](https://aldena.ai/integrations/git): install git on a room's server and its agents work a real checkout: branches, commits, diffs, history and rebases, on the machine that runs the test suite. - [postgresql ai agent: a real database on the room's own server](https://aldena.ai/integrations/postgresql): install postgresql on a room's server and its agents run psql, apply migrations, seed data, and run the test suite against a database that room owns alone. - [redis ai agent: a live cache and queue on the room's own server](https://aldena.ai/integrations/redis): install redis on a room's server and its agents run redis-cli, inspect keys, exercise queues and rate limiters, and test the caching path on a real instance. - [mysql ai agent: a mysql server the room owns and agents can run](https://aldena.ai/integrations/mysql): install mysql community server on a room's server and its agents apply migrations, seed fixtures, run queries with the mysql client, and test live. - [mariadb ai agent: a mariadb server per room for migrations and tests](https://aldena.ai/integrations/mariadb): install mariadb on a room's server and its agents run the mariadb client, apply migrations, seed fixtures, and test queries on a live database. - [sqlite ai agent: agents that open the file and query it directly](https://aldena.ai/integrations/sqlite): install sqlite on a room's server and its agents open databases with the sqlite3 cli, inspect schemas, run one-off queries, and work with the db file you ship. - [mongodb ai agent: a mongodb server on the room's own machine](https://aldena.ai/integrations/mongodb): install mongodb community server on a room's server and its agents load fixtures, run queries and aggregations, check indexes, and test against a live instance. - [elasticsearch ai agent: index, query, and tune relevance in a room](https://aldena.ai/integrations/elasticsearch): install elasticsearch on a room's server and its agents create indices, load documents, run queries against the real analyzer, and tune the mappings. - [chrome ai agent: headless chrome on the room's own server](https://aldena.ai/integrations/chrome): install google chrome stable on a room's server and its agents drive a real browser headless, render pages, print to pdf, and check what a page returns. - [agent browser: managed headless chrome your agents drive](https://aldena.ai/integrations/agent-browser): install agent browser on a room's server and its agents drive a managed chrome from the command line: open pages, interact, and read what the browser rendered. - [php ai agent: agents that run your php code, not describe it](https://aldena.ai/integrations/php): install php on a room's server and its agents run scripts, check loaded modules, start a dev server, and iterate on real output instead of guessing at the code. - [go ai agent: agents that build, test, and vet a go module for real](https://aldena.ai/integrations/golang): install the go toolchain on a room's server and its agents run go build, go test, go mod tidy and gofmt against the module, reading real compiler output. - [rust ai agent: agents that let the borrow checker do the reviewing](https://aldena.ai/integrations/rust): install the rust toolchain on a room's server and its agents run cargo build, cargo test and cargo check against the crate, working from real diagnostics. - [python ai agent: agents that write the script and then run it](https://aldena.ai/integrations/python): install python on a room's server and its agents get python3, pip3 and venv through the bash tool, so they build environments and run the code they write. - [java ai agent: a jdk on the room server so agents can build and test](https://aldena.ai/integrations/java): install eclipse temurin on a room's server and its agents compile with javac, run with java, and drive the project's own maven or gradle wrapper. - [pm2 ai agent: agents that keep a process running and read its logs](https://aldena.ai/integrations/pm2): install pm2 on a room's server and its agents start long-running processes, list what is running, read the logs, and stop everything cleanly when work is done. - [dotnet ai agent: the .net sdk on the room server for build and test](https://aldena.ai/integrations/dotnet): install the .net sdk on a room's server and its agents restore, build, test and run projects with the dotnet cli against the checkout already on the machine. - [ruby ai agent: agents that run the script and the bundle for real](https://aldena.ai/integrations/ruby): install ruby on a room's server and its agents run scripts, install gems, drive bundler with bundle exec, and iterate on the error the interpreter produced. - [nginx ai agent: write the config, test it on a live server](https://aldena.ai/integrations/nginx): install nginx on a room's server and its agents write server blocks, validate the config, reload the service, and check what the proxy actually returns. - [apache ai agent: agents that edit the vhost and check what it serves](https://aldena.ai/integrations/apache): install the apache http server on a room's server and its agents edit virtual hosts, enable modules, restart the service, and check what a request returns. - [docker ai agent: write the dockerfile, bring the stack up](https://aldena.ai/integrations/docker): install docker engine with buildx and compose on a room's server and its agents build images, run containers, bring stacks up, and read the build logs. - [rabbitmq ai agent: a live broker for testing queues and consumers](https://aldena.ai/integrations/rabbitmq): install rabbitmq on a room's server and its agents publish messages, run consumers, inspect queues with rabbitmqctl, and prove a retry path actually works. - [kafka ai agent: a kraft broker in the room for producers and consumers](https://aldena.ai/integrations/kafka): install apache kafka in kraft mode on a room's server and its agents create topics, produce and consume messages, and inspect consumer groups. ## Agent pages - [ai assistant agent: sets the room up, keeps the facts straight](https://aldena.ai/agent/ai-assistant): iris is the room steward. it records the repository, client and stack facts every agent reads, clones the repo onto the room server, and does the setup chores. - [ai project manager: takes the request, routes it, reports back](https://aldena.ai/agent/ai-project-manager): sage is the front door of a room. it routes requirements to the analyst and delivery to engineering, then returns the pull request and release notes. - [ai business analyst: asks the questions first, then writes the spec](https://aldena.ai/agent/ai-business-analyst): vera clarifies scope with you before it writes anything, then produces a prd with atomic user stories, acceptance criteria, assumptions and open questions. - [ai scrum master: turns an approved spec into a backlog you sign off](https://aldena.ai/agent/ai-scrum-master): atlas turns an approved spec into atomic work items, shows you the whole backlog for sign-off, and only then creates tickets in the connected tracker. - [ai engineering manager: delegates the build and opens the pull request](https://aldena.ai/agent/ai-engineering-manager): magnus runs the delivery pipeline. it delegates architecture, build, tests and review to its reports one at a time, then opens the pull request you review. - [ai solution architect: requirements turned into a buildable spec](https://aldena.ai/agent/ai-solution-architect): pallas reads the requirements, surveys the repo it will be built in, and writes an architecture doc with components, data model, api contracts and sequencing. - [ai backend developer: schema, migrations, services and endpoints](https://aldena.ai/agent/ai-backend-developer): dane owns the backend end to end: schema and migrations first, then services and endpoints with auth and validation, verified by curling what it built. - [ai frontend developer: pages, components, state and forms](https://aldena.ai/agent/ai-frontend-developer): lumen owns the frontend end to end. it builds pages, components, stores, data hooks and forms wired to the real api, then type-checks, lints and builds. - [ai full stack developer: one feature end to end across both lanes](https://aldena.ai/agent/ai-full-stack-developer): nova builds a whole feature in one pass: backend first with schema, migrations and endpoints, then the frontend wired to what it just built, no handoff. - [ai code reviewer: reads the diff across five lenses, never edits](https://aldena.ai/agent/ai-code-reviewer): argus reads the working diff through five passes: maintainability, security, performance, backend and frontend correctness. it reports findings, never edits. - [ai bug fixer: reproduce, root cause, smallest fix, regression test](https://aldena.ai/agent/ai-bug-fixer): orion reproduces the bug first, traces the root cause in the code, ships the smallest safe change, and adds the regression test that fails before the fix. ## Learn - [can you build an ai agent with chatgpt?](https://aldena.ai/learn/can-you-build-an-ai-agent-with-chatgpt): yes, three ways: custom gpts for simple assistants, agent builder for visual workflows, and the agents sdk in code. each has a different ceiling. - [can you hire ai employees?](https://aldena.ai/learn/can-you-hire-ai-employees): you can hire the software, not a person. you get a role you assign work to and pay monthly, with none of the employment law and none of the earned judgement. - [do companies hire ai agents?](https://aldena.ai/learn/do-companies-hire-ai-agents): yes, at scale, but mostly as software subscriptions rather than headcount. support, coding, and document processing lead. most pilots stall before production. - [does chatgpt have a coding agent?](https://aldena.ai/learn/does-chatgpt-have-a-coding-agent): yes, codex. it runs in the terminal, in the ide, and in the cloud, and it is a separate product from the code help inside a chatgpt conversation. - [does microsoft teams have an ai agent?](https://aldena.ai/learn/does-microsoft-teams-have-an-ai-agent): yes. copilot in teams summarises meetings and chats, and copilot studio lets you publish custom agents into teams as a chat participant. - [how can i create my own ai agent?](https://aldena.ai/learn/how-can-i-create-my-own-ai-agent): the loop is about eighty lines of code. pick a framework, give it two or three tools, add a step budget, and iterate on the tools rather than the prompt. - [how do i become an ai software engineer?](https://aldena.ai/learn/how-do-i-become-an-ai-software-engineer): get strong at backend engineering first, then ship three real systems with retrieval, evaluation, and cost control in them. six to twelve months from there. - [how much do ai software engineers get paid?](https://aldena.ai/learn/how-much-do-ai-software-engineers-get-paid): us bands in 2026: $130k to $180k entry, $180k to $260k mid, $250k to $400k senior, and $500k+ at frontier labs. europe runs roughly 40% lower. - [how much does it cost to hire an ai agent?](https://aldena.ai/learn/how-much-does-it-cost-to-hire-an-ai-agent): real numbers: build-your-own runs on token cost alone, platforms sit at $20 to $250 per seat per month, and enterprise pilots are quoted at $25,000 to $75,000. - [how to build agentic orchestration?](https://aldena.ai/learn/how-to-build-agentic-orchestration): start hierarchical with a shared state store, put a global step budget in first, and add agents one at a time only when a single one visibly fails. - [is ai replacing software developers?](https://aldena.ai/learn/is-ai-replacing-software-developers): not replacing, reshaping. code generation is strong and the job was never mostly typing. the real damage is to entry-level hiring, which is a pipeline problem. - [is chatgpt a multi-agent system?](https://aldena.ai/learn/is-chatgpt-a-multi-agent-system): no. chatgpt is one model with routing and tools. it borrows some multi-agent machinery internally, but you get a single agent, not a team you can arrange. - [is chatgpt an agent or llm?](https://aldena.ai/learn/is-chatgpt-an-agent-or-llm): both, at different layers. gpt is the model. chatgpt is the product wrapped around it, and in agent mode that product behaves as an agent. - [is chatgpt an agentic ai?](https://aldena.ai/learn/is-chatgpt-an-agentic-ai): partly. plain chat is not agentic. agent mode and deep research are, because they plan, act, and revise without a prompt per step. the same product does both. - [is copilot a multi-agent system?](https://aldena.ai/learn/is-copilot-a-multi-agent-system): copilot itself is one agent. the multi-agent parts of microsoft's stack are copilot studio and autogen, which are separate products you configure yourself. - [is it free to build an ai agent?](https://aldena.ai/learn/is-it-free-to-build-an-ai-agent): the frameworks are free. the models, hosting, and your time are not. a working prototype costs about $5 in tokens and two days. production is a different bill. - [is it hard to build an ai agent?](https://aldena.ai/learn/is-it-hard-to-build-an-ai-agent): the first version is an afternoon. the version you would leave running against production data is a quarter, and the difficulty is entirely in the second half. - [what 3 jobs will not be replaced by ai?](https://aldena.ai/learn/what-3-jobs-will-not-be-replaced-by-ai): skilled trades, hands-on healthcare, and roles carrying legal accountability. each survives for a different reason, and the reasons beat the list. - [what are agentic ai companies?](https://aldena.ai/learn/what-are-agentic-ai-companies): companies whose product is an agent that acts rather than answers. the category has four layers: model labs, frameworks, platforms, and applications. - [what are ai employees?](https://aldena.ai/learn/what-are-ai-employees): an ai employee is an agent packaged as a role rather than a tool: a job title, a standing queue of work, durable memory, and a monthly cost line. - [what are ai workers?](https://aldena.ai/learn/what-are-ai-workers): ai workers are software agents that carry out assigned work end to end. the term also covers cloudflare workers ai and the humans who label training data. - [what are enterprise ai agents?](https://aldena.ai/learn/what-are-enterprise-ai-agents): agents built for company deployment rather than personal use. the difference is not intelligence, it is identity, permissions, audit, isolation, procurement. - [what are the 7 types of ai agents?](https://aldena.ai/learn/what-are-the-7-types-of-ai-agents): the textbook list is five. the seven you see online adds hierarchical and multi-agent systems. here is each type, in plain language, and what it is used for. - [what are the ai coding agents?](https://aldena.ai/learn/what-are-the-ai-coding-agents): claude code, openai codex, github copilot agent, cursor, devin, and the open-source ones. sorted by whether they work beside you or run on their own. - [what are the top 10 agentic frameworks?](https://aldena.ai/learn/what-are-the-top-10-agentic-frameworks): langgraph, crewai, autogen, the openai and anthropic agent sdks, llamaindex, smolagents, pydanticai, semantic kernel, and mastra. sorted by what each is for. - [what are the top 5 ai assistants?](https://aldena.ai/learn/what-are-the-top-5-ai-assistants): chatgpt, claude, gemini, microsoft copilot, and perplexity. each is genuinely best at something different, and the split is clean enough to be useful. - [what does an ai software engineer do?](https://aldena.ai/learn/what-does-an-ai-software-engineer-do): builds software that uses models: retrieval, evaluation, inference plumbing, and agent loops. mostly ordinary engineering with two unusual problems attached. - [what is a $900000 ai job?](https://aldena.ai/learn/what-is-a-900000-ai-job): the $900,000 ai job is a real but rare pay band: senior research and infrastructure roles at frontier labs, where most of the number is equity, not salary. - [what is a multi-agent system?](https://aldena.ai/learn/what-is-a-multi-agent-system): several autonomous agents sharing an environment and coordinating toward a goal. the term is 40 years old and predates language models by decades. - [what is agentic ai?](https://aldena.ai/learn/what-is-agentic-ai): agentic ai is a model that plans, calls tools, checks results, and keeps going until a goal is met. the difference from a chatbot is the loop, not the model. - [what is agentic orchestration?](https://aldena.ai/learn/what-is-agentic-orchestration): coordinating several agents toward a goal without scripting the steps. the orchestrator assigns work, routes results, and decides when the whole thing is done. - [what is ai staff?](https://aldena.ai/learn/what-is-ai-staff): software agents filling staff roles rather than acting as tools. the phrase also means human ai teams and ai staffing agencies, worth untangling first. - [what is an agent swarm?](https://aldena.ai/learn/what-is-an-agent-swarm): many simple agents producing useful behaviour through local rules and no central controller. in llm practice it usually means several agents with handoffs. - [what is an ai agent marketplace?](https://aldena.ai/learn/what-is-an-ai-agent-marketplace): a catalog where you browse and install prebuilt agents or agent skills. four kinds exist, and only one solves the problem buyers think it solves. - [what is an ai software developer?](https://aldena.ai/learn/what-is-an-ai-software-developer): two meanings: a human who builds software with models in it, or an agent that writes and ships code. context decides which, and the two are usually conflated. - [what is an ai teammate?](https://aldena.ai/learn/what-is-an-ai-teammate): an agent positioned as a colleague rather than a tool: it sits in your channels, holds context about the work, and takes assignments instead of prompts. - [what is an example of a multi-agent system?](https://aldena.ai/learn/what-is-an-example-of-a-multi-agent-system): concrete examples, from traffic lights and warehouse robots to a software team of planner, engineers, and reviewer working one codebase together. - [what is an example of ai orchestration?](https://aldena.ai/learn/what-is-an-example-of-ai-orchestration): three worked examples: a support ticket routed and resolved, a document pipeline, and a feature shipped by a planner, two engineers, and a reviewer. - [what is orchestration in ai?](https://aldena.ai/learn/what-is-orchestration-in-ai): the layer that decides what runs, in what order, with what data, and what happens when a step fails. it covers three things people use the same word for. - [what is the 30% rule for ai?](https://aldena.ai/learn/what-is-the-30-percent-rule-for-ai): there is no single 30% rule. the phrase covers three claims: an automation ceiling, a productivity gain, and a hallucination budget. only one is useful. - [what is the ai workforce?](https://aldena.ai/learn/what-is-the-ai-workforce): the ai workforce is the set of software agents a company staffs like headcount: named roles, assigned work, a reporting line, and a bill you can attribute. - [what is the best multi-agent system?](https://aldena.ai/learn/what-is-the-best-multi-agent-system): depends on whether you want a framework to build with or a platform to run. langgraph and crewai lead on frameworks. platforms differ on memory and gates. - [what is the difference between crewai and agent swarm?](https://aldena.ai/learn/what-is-the-difference-between-crewai-and-agent-swarm): crewai is a framework with roles, tasks, and a process. swarm is a pattern, and also an openai library, where agents hand off to each other with no coordinator. - [what is the difference between workflow and agentic orchestration?](https://aldena.ai/learn/what-is-the-difference-between-workflow-and-agentic-orchestration): a workflow decides the path in advance. agentic orchestration decides it at runtime. one is cheaper and debuggable, the other handles branches nobody listed. - [what jobs are in danger due to ai?](https://aldena.ai/learn/what-jobs-are-in-danger-due-to-ai): screen work that produces a document and needs no signature. entry-level rungs are going first, which is a bigger problem than whole occupations disappearing. - [what workers is ai replacing?](https://aldena.ai/learn/what-workers-is-ai-replacing): so far: data entry, basic translation, first-draft copy, tier-one support, and junior document review. mostly tasks rather than people, and mostly entry level. - [which 5 jobs will survive ai?](https://aldena.ai/learn/which-5-jobs-will-survive-ai): no list of five is authoritative. the roles that hold up share three traits: physical presence, legal accountability, or a relationship built into the work. - [which ai agent platform is best for enterprises?](https://aldena.ai/learn/which-ai-agent-platform-is-best-for-enterprises): depends which constraint binds. incumbent suites win on procurement, specialists win on outcome, and build-your-own wins only when the agent is your product. - [which is the best agentic ai?](https://aldena.ai/learn/which-is-the-best-agentic-ai): there is no single best. the answer splits four ways by what you are automating: coding, customer operations, enterprise workflow, or a whole team. - [which is the best ai agent?](https://aldena.ai/learn/which-is-the-best-ai-agent): no agent wins every job. sorted by what you need done: coding, browsing, research, support, and running a team. plus the test that beats every leaderboard. - [which is the best ai employee?](https://aldena.ai/learn/which-is-the-best-ai-employee): depends on the role you are filling. vendors sell canned personas that work solo. the honest test is a fortnight of real work with the completion rate recorded. - [who are the big 4 ai agents?](https://aldena.ai/learn/who-are-the-big-4-ai-agents): there is no official big 4 of ai agents. the phrase borrows from consulting. here is what searchers usually mean and which platforms belong in the comparison. - [who are the major developers of ai?](https://aldena.ai/learn/who-are-the-major-developers-of-ai): openai, anthropic, google deepmind, meta, microsoft, nvidia, mistral, alibaba, and deepseek. sorted by whether they build models, hardware, or both. - [who is leading in agentic ai?](https://aldena.ai/learn/who-is-leading-in-agentic-ai): three separate races: anthropic and openai lead on capability, microsoft and salesforce on distribution, and the application layer is wide open. ## Blog posts - [what it actually takes to self-host an always-on ai agent](https://aldena.ai/blog/self-hosted-ai-agent): the real bill of materials behind a self-hosted ai agent: gateway, sandbox, memory, browser, auth, and cost. and what changes when that job is someone else's. - [Your first AI employee: what changes in week one](https://aldena.ai/blog/first-ai-employee): Hiring an AI employee is a shift from tools you operate to teammates that deliver. What to delegate first, and why a small team beats a lone hire. - [An AI employee platform: hire roles, not build workflows](https://aldena.ai/blog/ai-employee-platform): What an AI employee platform does for a small team, where human review has to sit, and why hiring a role beats mapping a workflow. - [AI-assisted development: what changed on my team](https://aldena.ai/blog/ai-assisted-development): An honest report from building Aldena with its own agent teams: the roles, the guardrails, the review gate that stays human, and where the money goes. - [AI coding agents: direct and verify, don't ask and paste](https://aldena.ai/blog/ai-coding-agents-guide): A practical guide to getting real work out of AI coding agents. Plan before you delegate, scope small, review with suspicion, and trust tests over confidence. - [Multi-agent platforms: what to demand before you buy one](https://aldena.ai/blog/multi-agent-platforms): A buyer's guide to multi-agent platforms. How a platform differs from a framework, what to test in orchestration and permissions, and where guardrails go. - [Enterprise AI agents pay off when they become teams](https://aldena.ai/blog/enterprise-ai-agents): The business case for enterprise AI agents is teams, not smarter assistants. Where the value shows up, what governance has to exist, and how I would start. - [ai employees: what the term means and what it does not](https://aldena.ai/blog/ai-employees): an ai employee is an agent with a role, permissions, memory, and a review gate. I draw the line against chatbots, RPA and copilots, and show a real deployment. - [An AI workforce platform is a control plane, not a chatbot](https://aldena.ai/blog/ai-workforce-platform): What an AI workforce platform does, how hiring an AI worker differs from building one, and how Aldena models a workforce with rooms, hierarchy, and approvals. - [AI agent frameworks: what they give you, and what they don't](https://aldena.ai/blog/ai-agent-frameworks): What an AI agent framework actually provides, how crewAI, LangGraph, AutoGen, and the provider SDKs differ, and the infrastructure none of them ship. - [what is a multi-agent system? definition and components](https://aldena.ai/blog/what-is-a-multi-agent-system): a multi-agent system is several autonomous agents coordinating in a shared environment. the definition, the components, the use cases, and the failure modes. - [Multi-Agent Systems: How They Work and Who Needs One (2026)](https://aldena.ai/blog/multi-agent-systems): How multi-agent systems decompose work, coordinate, and fail in production, plus a plain test for when a single agent is the cheaper, better call. - [AI agent orchestration in production: what actually breaks](https://aldena.ai/blog/ai-agent-orchestration): How I orchestrate AI agent teams in Aldena: why hierarchy beats a flat swarm, how work travels, what shared memory and approval gates do, and where tokens go. - [How to build an AI agent team that ships one deliverable](https://aldena.ai/blog/how-to-build-an-ai-agent-team): A working AI agent team needs real roles, a coordinator that only delegates, bounded channels, shared memory, and cost-tiered models. Here is the build.