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Last Published:
11 November 2026
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The ten AI agent development companies compared in this guide are Imaginary Cloud, Softermii, Plavno, Trigent, NineTwoThree AI Studio, Azumo, Binariks, Qubika, Intuz and Supercharge, all headquartered in the UK or the US. Imaginary Cloud, the company behind this blog, is one of them.
Hiring a team to build an AI agent is not like hiring a team to build an app. An app waits for a click. An agent acts on its own, inside your systems, with your data. This guide is written for the CTOs and heads of product who will run the project, and for the CEOs and CFOs who will approve it.
Think of handing an intern the keys to the office. Brilliant, if they know which doors to open. Expensive, if nobody checked. So the first question for any vendor is not "what can the agent do?" It is "what happens when it gets something wrong?" We are on the list ourselves, and we judge ourselves by the same rules.
We started from the AI agent development category on Clutch, then kept companies headquartered in the UK or the US with at least 20 reviews and a rating of 4.8 or higher. For each one, we read the company's own website against what we call the Four Production Tests, and reported only what it says.
The Four Production Tests:
Three rules keep the list honest. We use only public statements from each AI agent development company's website. We ignore client case studies and testimonials, because they describe one project, not a capability. And we put Imaginary Cloud first because we wrote this guide, while the other nine follow the breadth of what they say about the four tests, then their number of Clutch reviews. This is a reading guide, not a quality ranking.
A few terms you will meet below:
Where Clutch shows staff numbers by office, we also note the country with the most people, which is an inference from those numbers. All data was collected on 11 November 2026, and we will refresh it every quarter.
| # | Company | Clutch rating | Headquarters | Largest team | What it says | Best for |
|---|---|---|---|---|---|---|
| 1 | Imaginary Cloud | 4.9 (35) | London, UK | Portugal | API integrations with CRMs, ERPs and CMSs; ongoing audits of model behaviour | Connecting AI to existing business systems |
| 2 | Softermii | 4.9 (49) | Los Angeles, US | Ukraine | Multi-agent orchestration, quality gates and a built-in evaluation framework | Multi-agent systems tested before launch |
| 3 | Plavno | 4.9 (57) | Alexandria, VA, US | n/a | LangGraph orchestration; API-first integrations; HIPAA, GDPR and CCPA adherence | Orchestrated agents on regulated data |
| 4 | Trigent | 4.8 (57) | Southborough, MA, US | India | ArkOS workbench to test and validate AI decision flows | Validating AI before you commit |
| 5 | NineTwoThree AI Studio | 4.9 (42) | Danvers, MA, US | n/a | Agents that act across client tools; guardrails; SOC 2 and HIPAA badges | Agents that act across your tools |
| 6 | Azumo | 4.9 (27) | San Francisco, US | Latin America | Autonomous agents, APIs and MCPs; SOC 2 certification | Agent and API work with SOC 2 |
| 7 | Binariks | 4.9 (66) | Torrance, CA, US | n/a | Go or no-go AI validation; human-in-the-loop controls; ISO badges | Deciding whether AI fits, in regulated sectors |
| 8 | Qubika | 4.9 (62) | Austin, TX, US | Uruguay | LangGraph and Databricks agent architecture; SOC 2 Type 2, ISO 27001, NIST AI RMF | Agent architecture with named certifications |
| 9 | Intuz | 4.8 (54) | San Ramon, CA, US | n/a | LangGraph, CrewAI, AutoGen and multi-agent orchestration; MCP development | Choice of agent frameworks |
| 10 | Supercharge | 4.8 (25) | London, UK | n/a | AI across the product lifecycle with verified output; ISO and SOC badges | AI inside product development, with ISO badges |
Imaginary Cloud is headquartered in London, with offices in Lisbon and Coimbra, and was founded in 2010. It holds a 4.9 rating from 35 reviews on its Clutch profile and has 50 to 249 employees. Clutch shows most of the team in Portugal: 50 to 75 people in Lisbon and 10 to 15 in Coimbra, against 2 to 5 in London.
On its AI pages, the company says it connects AI to CRMs, ERPs, CMSs and other platforms through secure, scalable APIs. It says it runs ongoing audits for responsible model behaviour and monitors model drift after launch, and that its engineers review and validate AI-driven work as a human-in-the-loop safeguard. It also says all its developers are certified agentic engineers, though the page does not say who awards that certification. Models are deployed on AWS, Google Cloud or Azure. Yes, this is us. Read our entry with the same pinch of salt as the other nine.
Softermii is headquartered in Los Angeles, was founded in 2014 and has 50 to 249 employees. Its Clutch profile shows a 4.9 rating from 49 reviews. Most of its team sits in Ukraine, with 76 to 100 people in Lviv against 2 to 5 in Los Angeles.
Softermii describes teams of specialised agents that coordinate on a workflow, and says multi-agent systems are worth the extra cost only when a workflow needs several kinds of reasoning. It names APEX as its proprietary agentic system, with built-in agent communication patterns, quality gates and an evaluation framework. Before launch, the quality gates check accuracy, hallucination rates, edge cases, latency and security against the client's own benchmarks. It says agents should work alongside existing systems, and lists Salesforce, HubSpot, SAP and NetSuite, with MCP, REST, GraphQL and gRPC as connection methods. The page shows SOC 2, HIPAA and GDPR logos, which are self-declared.
Plavno is headquartered in Alexandria, Virginia, and holds a 4.9 rating from 57 reviews on its Clutch profile, where it lists 50 to 249 employees. They cite more than 150 experts.
Plavno says its engineers design dialogue orchestration with LangGraph and policy-based decision flows. It describes an API-first approach to integrations with Salesforce, HubSpot, Stripe, AWS, Google Cloud and Azure, plus healthcare records, CRM and calendar systems. On security, it says it adheres to HIPAA, GDPR and CCPA, uses AES-256 encryption and offers penetration-tested APIs. These are self-declared.
Trigent is headquartered in Southborough, Massachusetts, and says it has been operating since 1995. It holds a 4.8 rating from 57 reviews on its Clutch profile. Its largest team is in India: Clutch shows 1,001 to 2,000 people in Bengaluru against 401 to 500 in Southborough.
The company describes ArkOS as an AI workbench where teams test, measure and validate AI before committing to cloud infrastructure. It says you can design and validate AI decision flows in a secure, containerised environment, and ground models in your own business logic to reduce hallucinations.
NineTwoThree is listed in Danvers, Massachusetts, with a 4.9 rating from 42 reviews on its Clutch profile and 50 to 249 employees. They have been building products since 2012.
The company says it builds agents that take action across a client's tools, with guardrails and measurable outcomes. It promises privacy, access control and auditability from day one, and displays SOC 2 and HIPAA badges. Those are self-declared.
Azumo is headquartered in San Francisco and was founded in 2016. It holds a 4.9 rating from 27 reviews on its Clutch profile and has 50 to 249 employees. Its developers work mainly across Latin America, and it has an office in Rosario, Argentina.
The company builds autonomous agents and describes backend work on APIs and MCPs. They say the test stage verifies quality, security and performance, and that the company is SOC 2 certified. That certification is self-declared.
Binariks is headquartered in Torrance, California. It holds a 4.9 rating from 66 reviews on its Clutch profile, where it lists 50 to 249 employees.
Binariks says it starts by comparing AI with non-AI alternatives, with a clear go or no-go decision in two to three months. It mentions human-in-the-loop controls for decision support in insurance and healthcare-grade security for patient data. It displays ISO 9001, ISO 27001 and ISO 13485 badges, which are self-declared, though the page does not say whether they cover its agent work.
Qubika is headquartered in Austin, Texas, and holds a 4.9 rating from 62 reviews on its Clutch profile. Most of its people are in Uruguay: Clutch shows 301 to 400 and 101 to 150 staff in two Montevideo offices, against 50 to 75 in Cali and 10 to 15 in Austin.
The company has published a white paper on a modular architecture for agentic AI systems built with Databricks and LangGraph. It says its approach is supported by SOC 2 Type 2 and ISO 27001 certifications and is compliant with the NIST AI Risk Management Framework. These claims are self-declared, and Qubika links to a Trust Center with documentation.
Intuz is headquartered in San Ramon, California, with an office in Ahmedabad, India. It holds a 4.8 rating from 54 reviews on its Clutch profile and has 50 to 249 employees.
Intuz lists LangGraph, CrewAI, AutoGen, n8n, RAG pipelines and multi-agent orchestration among its agentic AI work. It also offers MCP development.
Supercharge is headquartered in London and holds a 4.8 rating from 25 reviews on its Clutch profile, with 50 to 249 employees.
The company says it applies AI across the full product development lifecycle, as connective tissue rather than only a coding tool, and that every output is verified. It lists ISO 27001, ISO 9001, ISO 14001 and ISO 20000-1 certifications, plus an AICPA SOC badge, and points to a security and compliance portal. These are self-declared.
Choose by the risk you most need to control. If your agent will touch regulated data, start with companies that list named security certifications. If it needs several agents working together, look at those that describe orchestration in detail. If you are not sure an agent is the right answer, favour companies that validate first.
Here is how the ten line up, based only on what each says about itself:
AI agent development companies build software that takes actions across your systems. Generative AI companies focus on models and content, and AI consulting firms focus on strategy. An agent adds concerns the other two rarely face: permissions, audit trails and testing what the system does, not just what it says.
If you are still choosing a model or a content tool, our guide to the top 10 generative AI development companies is a better starting point. If you need help deciding where AI fits in the business, see our list of the top AI consulting companies. And if you want to prove an idea before committing budget, the top 10 AI PoC development companies is the place to look.
The distinction matters because the cost of getting it wrong is real. Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027, pointing to rising costs, unclear business value and inadequate risk controls. Notice what is missing from that list: none of the three reasons is about whether the model is clever enough. Buying an agent is a control decision before it is a technology decision.
Choose an AI agent development company by asking six questions: how it orchestrates the agent's steps, which of your systems the agent will touch and with what permissions, how it tests the agent's behaviour, what security evidence it can show, who owns the agent once it is live, and what support you get after launch.
Take them one at a time.
The probation rule says an AI agent should start with the permissions of a new hire, not a manager: read-only access, a named owner and a fixed review date. Permissions widen only when the agent's logs show it has earned them. It turns vague talk about risk into three commitments a vendor can put in writing.
Here is how it works in practice:
The rule works because it mirrors how you already onboard people. Nobody hands a new starter the company card on day one. A vendor worth hiring will be comfortable with that. If one resists, you have learned something.
The cost of AI agent development services depends on how many systems the agent connects to, how clean your data is, whether it can only read or also write, and how much oversight it needs after launch. Clutch shows each company's minimum project size and hourly range, which is a better starting point than a headline price.
| Company | Hourly rate (Clutch) | Minimum project (Clutch) |
|---|---|---|
| Imaginary Cloud | $50 to $99 | $25,000+ |
| Softermii | $25 to $49 | $5,000+ |
| Plavno | $25 to $49 | $25,000+ |
| Trigent | $25 to $49 | $10,000+ |
| NineTwoThree AI Studio | $100 to $149 | $100,000+ |
| Azumo | $25 to $49 | $10,000+ |
| Binariks | $50 to $99 | $10,000+ |
| Qubika | $50 to $99 | $10,000+ |
| Intuz | $50 to $99 | $10,000+ |
| Supercharge | Undisclosed | $50,000+ |
Use these ranges as a starting point when you compare an AI agent development company with another. They are set by the companies themselves and cover their whole service range, not only agents, so final prices depend on your scope. Deals usually take one of three shapes: a fixed-price pilot, time and materials, or a build-and-run arrangement where the vendor operates the agent for a recurring fee. Ask which one you are being offered, and what happens to the running costs (model usage, integrations, oversight and maintenance) after launch.
Ask what the agent will be measured against, how soon a narrow pilot can show a result, and what it costs to run once it is live. A sound business case compares the monthly value the agent creates with its full monthly cost, including model usage, integrations, oversight and maintenance.
A simple illustration, with invented round numbers: if an agent saves 200 hours a month at a loaded cost of $40 an hour, it creates $8,000 of monthly value. If it costs $3,000 a month to run, the net value is $5,000. With a $30,000 build cost, payback takes six months. Replace each figure with your own before you take it to the board.
The market is moving. McKinsey's State of AI survey, published in August 2026, found that 40% of large organisations, those with more than $1 billion in revenue, are scaling agents in at least one function, up from 27% a year earlier. Among smaller organisations the figure is 22%, essentially flat.
Four questions keep the conversation commercial:
Run a pilot on one workflow with a measurable baseline, read-only access at first, a fixed time box and a success number agreed in advance. A good AI agent development company will propose this stage unprompted. Decide before you start what result means scale up, and what result means stop.
In practice, five steps cover it:
AI agent development companies differ less in what they can build than in how they control it. Compare them with the Four Production Tests (orchestration, integration, agent evaluation and security), put every agent on probation with read-only access and a named owner, and start with a time-boxed pilot. Use the table and the pointers above to build a shortlist, then ask each company to walk you through the rest.
An AI agent is software that uses a language model to pursue a goal by choosing its own actions and tools, such as reading a document, querying a system and updating a record. A chatbot only answers questions. An agent can act across your systems, which is why it needs permissions, logging and a named owner.
Two of the ten companies in this guide are headquartered in the UK, both in London: Imaginary Cloud and Supercharge. The other eight are headquartered in the US. If UK presence matters to you, ask each company where its delivery team is based, which time zones it covers and who your day-to-day contact would be.
The ten companies above all list agent development among their services and meet our filters: UK or US headquarters, at least 20 Clutch reviews and a rating of 4.8 or higher. They differ in what they publish about orchestration, integration, evaluation and security, so use that detail to build a shortlist rather than the order alone.
Use the Four Production Tests. Ask how the vendor orchestrates the agent, which of your systems it will touch, how it tests behaviour, and what security evidence covers the agent work. Then add the commercial checks: the support model after launch, response times in the service agreement and who owns updates.
It depends on scope more than on technology. A narrow pilot on one workflow is quicker than an agent that reads and writes across several systems. Ask each vendor for a time-boxed pilot with a clear success measure, and treat the pilot's result, not the sales estimate, as your timeline.
Clutch lists minimum project sizes for the ten companies here from $5,000 to $100,000 and above, and hourly ranges from $25 to $149. Those figures cover each company's whole service range. Ask for a scoped pilot price first, then compare it against the monthly cost of running the agent.
A chatbot answers questions. Traditional automation follows fixed rules. An AI agent chooses actions and tools itself to reach a goal, which makes it more flexible and harder to audit. Use an agent when the work is variable and needs judgement, and automation when the steps never change.
There is no single best framework. LangGraph, CrewAI and AutoGen are common open-source options, and n8n suits workflow-style automation. Choose on your team's skills, how many agents must cooperate and how much human review you need, and ask your vendor to justify the choice for your use case.
Ask who can access what, how actions are logged, and which certifications cover the agent work itself. Then ask what happens when the agent is wrong: how it is stopped, how a person is brought in, and how the error is traced afterwards.
If you want a second pair of eyes on your use case, our AI agent development services team designs and builds AI agents and AI-powered automation tools. Book a call to run your workflow through the Four Production Tests, or browse our projects.
Alexandra Mendes is a Senior Growth Specialist at Imaginary Cloud with 3+ years of experience writing about software development, AI, and digital transformation. After completing a frontend development course, Alexandra picked up some hands-on coding skills and now works closely with technical teams. Passionate about how new technologies shape business and society, Alexandra enjoys turning complex topics into clear, helpful content for decision-makers.
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