Until 2024, picking an AI generation service was simple. "Which one makes the prettier image?"
By 2026 that question has stopped being useful. The leading services run on largely the same engines. Open any platform and the newest image and video models are already on the list. Quality no longer separates them.
A new question replaced it. "How much of the work does it do on its own?"
Take a one-line brief, settle on a concept, choose which model to use, generate the image, extend it into video, add the captions — every platform is now racing to add a layer that does this for you. That layer is usually called an agent.
The problem is that a single word, "agent," covers four completely different things. Higgsfield's agent, FLORA's agent, and Carat's mini-agent share a label but not a job description. Pick one from a recommendation list and you can easily end up with something other than what you pictured.
This article looks at the notable services, sorts them into four types, and explains who each type fits. At the end, it places XBRUSH on that map.
Researched August 2026. Pricing and features in this category change often. Always reconfirm rates and credit amounts on each service's official page.
First: the three things an "agent" actually does
Let me narrow the term before comparing. What marketing copy calls an agent usually does one or more of the following.
| Capability | What it does | What you do without it |
|---|---|---|
| ① Planning | Breaks "one ad" into shots, copy, and a scene list | Write the brief and split the shots yourself |
| ② Model routing | Picks the engine that suits the task | Choose from the model list every single time |
| ③ Multi-step execution | Chains generate → animate → lip-sync → upscale | Download each result and re-upload it to the next tool |
All three together is "fully autonomous"; ② alone is "model abstraction"; ③ alone is "workflow automation." The four types below differ in how they combine these axes.
Type 1. Pipeline automation — from one line of chat to a finished asset
You hand over a brief and the agent drives planning, model selection, and rendering all the way to the end. The highest autonomy of the four — and the hardest to reach into midway.
Higgsfield Supercomputer
The flagship of this type. Its official page describes itself as "a complete AI creative team": describe a reel, an ad, a product shot, or a short film, and it plans it, picks the models, and renders inside a single conversation.
Three things stand out structurally.
- An agent built for orchestration — not a chat model, but an agent (Hermes) specialized in calling and combining roughly 40 tools. It chains them recursively — script generation → character consistency → clip generation — with a feedback loop that adjusts parameters and re-runs when a result misses.
- Memory — beyond short-term working memory, it keeps long-term memory for brand identity and episodic memory that accumulates past successes and failures. The parameters improve as similar requests repeat.
- Interfaces that open outward — MCP and CLI support lets you trigger generations from inside developer tools like Claude Code, and Photoshop and DaVinci Resolve plugins let you create assets inside your editing timeline. (explainX analysis, Masonry review)
Pricing — as of 2026, billed annually: Starter around $15/mo, Plus $39/mo, Ultra $99/mo, with roughly 200 / 1,000 / 3,000 monthly credits. Top-up packs run about $5 per 100 credits and expire in roughly 90 days. (Layer3Labs summary)
Good when — you ship ads and UGC at volume and turnaround matters more than the details of any single shot.
Frustrating when — you want to fix "just this part of this shot." High autonomy means few places to intervene, and going back costs credits again.
Same type: Pollo.ai
Pollo.ai also bundles several video engines (Kling, Veo, Seedance) and layers an agent on top that plans and generates viral videos automatically. Narrower in scope than Higgsfield and focused on video.
Type 2. Node canvas — you can see the workflow the agent built
The opposite end from Type 1. The process is laid out on screen as a node graph. Each node takes inputs, runs a model, emits outputs, and you wire them together. The agent will go as far as "connect these nodes like this" — and then you can take the graph apart yourself.
FLORA / FAUNA
Released in spring 2026, FAUNA is a creative agent that places 50-plus models across image, video, text, and utility categories on a single node canvas. The agent plans which models to use in what order, and when a result misses you swap one node and re-run. The company says it is in production use at Nike, Netflix, and Pentagram. Pricing is $18/mo Starter, $54/mo Studio, and $200/mo Scale, notable for unlimited seats and credit rollover. (launch coverage)
Figma Weave (formerly Weavy)
Figma acquired the node-canvas startup Weavy — reportedly around $150–200M — and shipped it as Figma Weave. You feed the same prompt to several models at once, compare the outputs side by side, and continue down whichever branch you like. Figma separately added an agent that edits designs on the canvas through natural language. (TechBuzz)
For UI and product design teams, the fact that it already lives inside Figma is the strongest argument.
Krea — Nodes
Krea offers 60-plus image, video, and 3D models including its own Krea 2, plus a separate real-time node canvas called Krea Nodes. Results respond immediately as you move parameters, which makes it strong for concept exploration. Entry pricing around $9/mo is the lowest in this group.
Good when — your team reuses the same workflow repeatedly. A graph you got right once carries straight into the next campaign, and you can hand a teammate a picture of how it was made.
Frustrating when — you are in a hurry. You have to understand the nodes before you can touch anything, and the learning curve is clearly steeper than Type 1.
Type 3. Design-output agents — give it a brief, get a full set back
Here the goal is not "one good image" but "a usable bundle of design deliverables." It targets work like posters, social sets, and brand kits, where several formats and variations are needed at once.
Lovart
It bills itself as "the world's first AI design agent." Instead of returning one image per prompt, it takes a creative brief and breaks the job down the way a designer would: set the concept, produce drafts, deliver a finished asset set.
- You work on an infinite canvas and edit generated results by layer — change only the text, or adjust a single element.
- Batch generation of up to 40 images at once pulls variant drafts in one pass.
- Brand identity generation is a separate feature, bundling logo, color palette, typography, and guidelines.
Pricing — there is a free tier; Pro runs about $72/mo with roughly 11,000 credits. Annual billing takes about 19% off. Note that unused credits do not roll over. (official pricing page)
Good when — branding and campaign work that needs "a full set." Per-format resizing and variations get sorted in one go.
Frustrating when — video-heavy work. Credits also burn fast, so if you iterate on many drafts the cost adds up sooner than expected.
Type 4. Conversational all-in-one — filling in the brief through Q&A
The previous three split on "how autonomous is it." This type splits on "how easy is it." So that you do not need to know how to write a good brief, the agent asks you questions first and collects what it needs.
Carat
Among Korean services this direction is clearest. Carat gathers 25-plus AI engines from around the world into a single chat window and says it has been used by more than three million people.
The core is the mini-agent. Rather than writing prompts, you simply answer the questions the AI asks, and a plan, images, video, and audio come together inside one conversation. (official docs)
Its distinguishing points are a Korean-language interface that handles Korean prompts naturally, and a pitch built around cutting costs sharply versus agency or studio production.
Good when — you find prompt writing a burden: solo marketers, small business owners, non-specialist practitioners.
Frustrating when — a request falls outside the fixed question set. Anything the questionnaire does not cover leaves you with less freedom.
The comparison on one screen
| Service | Type | What the agent does | Strength | Reference price (monthly, Aug 2026) |
|---|---|---|---|---|
| Higgsfield Supercomputer | Pipeline automation | Plan + model selection + multi-step execution | Brief-to-asset turnaround, MCP and plugin integration | $15 / $39 / $99 |
| FLORA (FAUNA) | Node canvas | Workflow design + combining 50 models | Reproducibility, unlimited seats, credit rollover | $18 / $54 / $200 |
| Figma Weave | Node canvas | Side-by-side multi-model comparison + canvas editing agent | Drops straight into a Figma workflow | Figma plan + credits |
| Krea (Nodes) | Node canvas | Real-time node experimentation | Low entry price, fast exploration | From $9 |
| Lovart | Design outputs | Brief → draft set + brand kit | Layer editing, 40-image batches, format handling | Free / Pro $72 |
| Carat | Conversational all-in-one | Collects requirements through Q&A, then generates | Korean language, 25+ engines, low learning cost | Free tier + paid plans |
| XBRUSH | Conversational all-in-one + editing | Natural-language request → generation, then hands off to editing and collaboration | Every function exposed as an API, so automation and manual control run together — plus sessions, folders, and shared live editing | Free / $7 / $20 / $34 |
What the table shows is not a ranking but an axis. As a rule, more autonomy means fewer places to intervene, and more control means more hands-on work. The question is not "which service is better" but "which way do I work."
That trade-off is not a law, though. Leave the existing manual controls in place and layer an agent on top of them, and you can have both. There is an example further down.
Pricing — before you line the numbers up
One thing needs saying first. Credit rates cannot be compared across services.
The same "1,000 credits" is 100 images in one place and three videos in another. Consumption varies by model, and a single 4K video draining hundreds of credits is common. So comparisons that rank services by "cost per credit" are mostly wrong.
Three things can actually be compared: the entry price (cheapest paid tier), the billing structure (rollover and seats), and what happens when you run out.
| Service | Free | Entry paid tier | Higher tiers | Unused credits | Seats |
|---|---|---|---|---|---|
| XBRUSH | 100 credits on signup (up to 400 with events) | Basic $7/mo · 2,000 credits | Plus $20 (6,500) / Pro $34 (12,000) | Reset monthly | 2 free → 5 on Pro, +1 per add-on pack |
| Krea | Limited free | About $9/mo | Higher plans separately | Varies by plan | Varies by plan |
| Higgsfield | Limited free | Starter $15/mo · 200 credits | Plus $39 (1,000) / Ultra $99 (3,000) | Top-ups expire in ~90 days | Varies by plan |
| FLORA | Limited free | Starter $18/mo | Studio $54 / Scale $200 | Rolls over | Unlimited |
| Lovart | Starter credits | Lower plans separately | Pro $72/mo · 11,000 credits | Does not roll over | Varies by plan |
| Carat | Free tier available | Paid plans separately | — | Varies by plan | Varies by plan |
Here is where they diverge.
- Cost of starting — XBRUSH and Krea begin under $10, Higgsfield and FLORA start at $15–18, and Lovart's Pro is $72. That is a different threshold for "let's just try it across the team."
- When headcount grows — FLORA's seats are unlimited, so adding people does not change the bill; XBRUSH attaches teammates one at a time through add-on packs. Services billed per seat get steeper as a team grows.
- Leftover credits — FLORA rolls them over, Lovart and XBRUSH reset monthly, and Higgsfield top-ups disappear after about 90 days. If your workload swings month to month, this line matters more than it looks.
XBRUSH takes 20% off for annual billing, which puts Basic at $65/year, Plus at $195/year, and Pro at $325/year. When usage falls short you add monthly packs (Basic $7 · Image $34 · Video $102) rather than moving your whole plan up a tier, so you extend only where you are short. (XBRUSH pricing)
Where XBRUSH sits on this map
XBRUSH starts from Type 4, the conversational all-in-one side. The flow it leads with is speak → create → continue together, and the entrance is making images, video, and audio from one line of natural language.
Four things separate it from the rest.
1. The agent was layered on top of the tools, not in place of them
This is where the trade-off above breaks.
XBRUSH built every generation and editing function as an API and had the agent call those APIs. Write in natural language in the chat window and the agent reads the intent, decides whether the job is generation or editing, picks the engine that fits, rewrites the prompt into something the model understands, and runs it.
What matters is that the manual controls were not removed. The workspace holds the chat window and the left-hand panel side by side — image, video, and audio tabs; generate, edit, and outpaint; AI engine, image size, and count, all set directly. Hand the draft to the agent and change only the settings that bother you in the panel.
The same functions are open externally through the XBRUSH Public API — image generation, editing, upscaling, background removal, video generation and upscaling, plus session, folder, and output management. That is why "the agent works at the API level" is not a metaphor.
So raising automation does not cost you control. Both sit on the same screen. (→ Just Say What You Want: How the XBRUSH Workspace AI Agent Handles Image, Video, and Audio)
2. Editing begins where generation ends
Most agent services stop at "generation." Even when a result is 90% right, the remaining 10% moves to a different tool. XBRUSH keeps retouching — inpainting, outpainting, upscaling, background removal — and a timeline and layer editor inside the same workspace. Generate, revise, and finish stay in one place.
However good the agent is, a person does the final pass. Where that pass happens is what decides your actual working time.
3. Work accumulates in sessions and folders, and the team continues in the same session
Once an agent speeds up generation, the next bottleneck forms immediately behind it. More people making things means more output, and it scatters into everyone's download folder. That is how you end up spending 30 seconds pulling eight drafts and three days agreeing on one.
This is why XBRUSH ships sessions, folders, and teams alongside the agent rather than after it.
Work accumulates in sessions — results do not fall out as loose files; they build up inside a task (session). Because the flow from image to video to audio stays as context in the same session, instructions that point back at an earlier result — "make the background brighter," "turn this into a video" — just work. Output created through the external API lands in the same session too.
Folders keep it organized — sessions and outputs go into folders, split by project, client, or campaign. You can keep nesting subfolders under Home, and dragging from the output feed files them. Teams or individual folders can be shared with other users by email, so you can open exactly one project and leave the rest closed.
The base unit is a team, not a person — each team has its own members, billing, and content library. Results land in the team library rather than a personal account, so the work survives whoever made it. You start with 2 members on the free plan, reach 5 on Pro, and add more through packs. Permissions come in four levels — Owner, Admin, Editor, Viewer — so a client can get Viewer and a freelance designer Editor: involved in the work, but unable to touch billing or team composition.
Teammates work inside the open session — this is the sharpest break from other services. A teammate can enter a session that is already open and retouch, edit, or regenerate directly. The output detail view carries the full prompt plus the model, seed, and sampling count, so someone who did not make it can copy the prompt, pick up where it left off, and lock the seed to reproduce the same tone. From that same screen they move straight into remix, upscale, edit, outpaint, background removal, or video generation.
Feedback sits next to the output — it does not migrate to a messenger or an email thread. Comments attach to the individual generation task. Mid-work you can leave "drop the background tone one step on this cut" right beside the image; a teammate sees it, replies, and produces the revision on the spot. Nobody has to describe coordinates, and the reason a draft won stays attached to the draft. The conversation log also records who ran what.
In short, generation and editing are themselves collaborative. A solo agent is fast, but prompts and drafts scatter across personal accounts and never accumulate as team assets. (→ Generating in 30 Seconds, Deciding in 3 Days: Why an AI Agent Needs Team Infrastructure)
4. The starting point for ad creative — models with rights cleared
This one sits on a different axis entirely. XBRUSH runs an IP Market, and the AI ad models listed there come with likeness and contract terms already settled. AI Studio features like Talk-to-You and Cinema take those models and build lip-sync ad videos or multi-cut ads.
Now that generation quality has levelled up across the board, what actually blocks advertising and commerce work is not "does it look good" but "am I allowed to use this face." It is also the box pipeline-automation services have largely left empty.
So: XBRUSH's place
While other services pick a side between autonomy and control, XBRUSH chose not to trade one for the other. Higgsfield-style automation that runs from a single line, and FLORA-style control that lets you rework the process yourself, both live in the same workspace — because the manual functions were not stripped out but opened up as APIs, with the agent running on top of them.
Bound into that is the stretch where you start by talking, a person finishes, and the team picks it up. That is the stretch where teams who have to ship to real channels — commerce, advertising, brand marketing — realistically spend the most time.
Choosing by situation
| Your situation | Matching type | Candidates |
|---|---|---|
| You ship dozens of UGC and ad assets every week | Pipeline automation | Higgsfield Supercomputer |
| Your team reuses the same production process | Node canvas | FLORA, Krea Nodes |
| You already work in Figma | Node canvas | Figma Weave |
| You need a brand kit or campaign set as one bundle | Design outputs | Lovart |
| Prompt writing is a burden and you want to move fast in Korean | Conversational all-in-one | Carat, XBRUSH |
| You want the automation, still set the details yourself, and need teammates editing inside the same task | Conversational all-in-one + editing | XBRUSH |
Running several at once is not a strange choice either — exploring in Krea and finishing elsewhere is a common combination. The cost that comes with it is that the more subscriptions you add, the more your output scatters.
What none of them can do yet
Regardless of type, as of August 2026 every agent stumbles in similar places.
- Taste — which of three drafts fits the brand is still a person's call.
- Fact checking — product specs, prices, and legal copy an agent writes in need review.
- Rights — training data, likeness, and trademark questions remain regardless of output quality.
- The last 10% — reaching 90% takes minutes, but the final stretch to something you can actually publish is still human work.
A separate article covers this in more depth → AI Agents Now Make Ads, Blogs, and Card News on Their Own — What Still Needs a Human
Summary
- In 2026 the deciding factor is the character of the agent, not model quality. The engines are much the same anyway.
- Agents split into four types — pipeline automation (Higgsfield), node canvas (FLORA, Figma Weave, Krea), design outputs (Lovart), and conversational all-in-one (Carat, XBRUSH).
- As a rule, more autonomy is faster but leaves fewer places to intervene, and more control is more precise but carries a heavier learning cost.
- That trade-off is not inevitable. XBRUSH exposed every generation and editing function as an API and layered the agent on top, putting natural-language automation and direct control on the same screen.
- On top of that sit session and folder management and team collaboration: output accumulates in sessions, files into folders, and teammates enter an open session to retouch, edit, or regenerate directly while leaving comments beside the output. Rights-cleared ad models from the IP Market come with it.
Frequently Asked Questions
What is the difference between an agent-powered generation service and a regular image generator?
A regular generator returns one output per prompt. An agent-powered service breaks the request into stages (planning), picks a suitable model for each stage (routing), and runs generation through animation and upscaling in sequence (multi-step execution). You do not have to download a file at each step and re-upload it into another tool.
How does Higgsfield Supercomputer differ from Lovart?
The target deliverable differs. Higgsfield focuses on driving a video and ad pipeline end to end in a single conversation, while Lovart takes a brief and returns a bundle of design deliverables — posters, social sets, brand kits — in a layer-editable form. Choose Higgsfield for video-centric work and Lovart for branding and graphics.
Are node canvas services hard for beginners?
The first step is definitely harder. But the agents in FLORA and Figma Weave assemble the node structure for you, so you can start by running the graph as built and change one node at a time as you get comfortable. For a team that will reuse the same workflow, the upfront learning cost is likely to pay back.
Which agent services are convenient to use in Korean?
The Korean services Carat and XBRUSH have the advantage in Korean-language interfaces and Korean prompt handling. Carat reduces the prompt-writing burden with a mini-agent that asks you questions, and XBRUSH lets you generate from a natural-language request and then continue into editing and team sharing in the same workspace.
What should I look at when comparing pricing for agent generation services?
Ranking by cost per credit is mostly meaningless, because consumption varies so much by model that the same credits can mean 100 images or three videos. Three things are genuinely comparable: the entry price of the cheapest paid tier, whether unused credits roll over, and how seats are billed as your team grows. For reference, XBRUSH is $7/mo Basic, $20/mo Plus, and $34/mo Pro, with 20% off for annual billing.
Does using an AI agent mean losing direct control over the settings?
It depends on how the service is built. Highly autonomous pipeline-automation tools leave few places to intervene, so fine-tuning is harder. XBRUSH, by contrast, built every generation and editing function as an API and layered the agent on top, so the original manual controls are still there. You can hand a job to the agent in natural language and then set only the items you care about — engine, image size, count — directly in the left-hand panel.
Can several people continue the same generation task?
In XBRUSH, yes. Output accumulates in a task (session) inside the team library rather than a personal account, and a teammate can enter an open session to retouch, edit, or regenerate directly. The output detail view keeps the full prompt along with the model, seed, and sampling values, so someone who did not create it can pick it up; comments attach to the individual generation task so feedback stays beside the output. Permissions come in four levels — Owner, Admin, Editor, and Viewer.
Can I use the faces in an AI-generated ad video as they are?
Even a face produced by a generative model can raise questions about resemblance to real people, training data provenance, and the scope of commercial use. The XBRUSH IP Market addresses this upfront by offering AI ad models whose likeness and contract terms are already settled. If you use another service, always check that platform's commercial-use terms and licensing conditions.
Services covered in this article
- Higgsfield Supercomputer — pipeline-automation creative agent
- FLORA / FAUNA — node canvas agent bundling 50-plus models
- Figma Weave (formerly Weavy) — multi-model node canvas built into Figma
- Krea Nodes — real-time responsive node canvas
- Lovart — brief-driven design-output agent
- Carat — Korean conversational all-in-one gathering 25-plus engines
- XBRUSH — all-in-one creative platform binding generation, editing, collaboration, and the IP Market into one workspace