AI Agents Development Company:How to Choose One, and a Ranked List

On November 11, 2026, the Claude service partner directory listed 354 companies. Each one is a possible AI agents development company for your project.

That count comes from one vendor program. Other builders are in a different program or in none. The list of possible builders is longer than any buyer can read.

The obvious question is short: which company is the best? I think that it is the wrong question. This guide gives a better one, and a method to answer it.

I am Chris Machetto. I run Frenchy Digital, and we build agents for clients. So I have a commercial interest in your choice.

For that reason, I did three things. I ranked eight other companies only on facts that you can check. I did not give my own company a score. I put our offer after the list, with its weak points.

This is the order of the guide. First, a better question and a test of whether you need a company at all. Then six checks that take about one hour for each company, and the ranked list. The guide ends with the proposal, the contract, the warning signs and a plan for this week.

Most buyers select an agent company the way that they select any agency. They look at the logos of past clients, the size of the team and the quality of the demo. That method is reasonable, because it works for many kinds of services.

It works less well here, for one reason. A demo shows that an agent can work one time. It does not show that the agent works on the hundredth unusual case in your data.

A wall of logos has a similar problem. It shows that a company did work for a brand. It does not show what the work was, or if it included an agent.

Think about a used car lot. When a buyer cannot see which cars are good, every seller says that each car is good. The useful signals cost a bad seller too much to fake: an inspection report, a warranty and a written history of repairs.

Agent companies have the same kind of signals. A vendor directory that requires certified people and production customers is one. A published price is another, because the company must then honor it. A contract clause that gives you the code is a third.

A percentage on a home page costs nothing to write. A list of awards costs little more.

Some buyers do not need an AI agents development company. Anthropic sells the models, and it says this directly in Building effective agents.

When building applications with LLMs, we recommend finding the simplest solution possible, and only increasing complexity when needed. This might mean not building agentic systems at all.

Anthropic, Building effective agents (December 19, 2024)

There are three routes to an agent, and only the last route needs a company like ours.

1. 1.Switch on an agent in software that you pay for now.: Check if your help desk or your CRM includes an agent. If it does, test it in draft mode first.
2. 2.Build the agent yourself on a no-code platform.: You pay a subscription and you give your own time. The limit is the list of connectors and actions that the platform offers.
3. 3.Hire a company to build a custom agent.: You pay for a project and for support after launch. In return, the agent can reach your own systems and follow your own rules.

The full comparison of the three routes, with list prices, is in our guide to AI agents for business. Read it first if you are not sure which route fits.

A company becomes necessary under three conditions. The agent must read or write in a system that no packaged product reaches. The agent must follow a rule that a settings page cannot express. Or you must own the code and the data.

If none of the three is true, stop here. A packaged agent costs less, and you can test it faster.

The word company covers very different organizations. The directory shows the range. Its 17 Global Premier partners include Accenture, Deloitte and Boston Consulting Group. Its 295 Select partners include firms that need only 10 certified people for that tier.

No row is better than the others. Each row fits a task, a budget and a level of risk.

The ranked list below has firms from three of the rows. I left the Global Premier firms out of the ranking. Their size makes them a different purchase, and a score would compare different things.

Do you consider a freelancer or a hire for your own team? I compare those routes in hire an AI agent developer: freelancer, agency or in-house. That article shows the arithmetic of a full salary.

One more distinction is important. A builder and a vendor are different. A vendor sells you a product that it owns. A builder makes something that you own, if the contract says so. This guide is about builders.

These six checks use only public pages. Each check takes about ten minutes. I used the same six checks for the ranked list below, so you can repeat my work.

I took eight companies from the Claude service partner directory and scored them with the six checks. None of them is in our earlier ranking of ten AI agent development companies, so the two lists together cover 18 firms.

What I excluded. I did not score accuracy, return on investment or any outcome percentage. The company itself is the source of every such number on these sites. I did not use review sites, because I did not check them today. I did not score the size of the team.

When and how. I read every page on November 11, 2026. Ties go to the higher directory tier, then to alphabetical order. To check a score, open the profile in the directory and the pages that I cite.

A score of zero means that I did not find the fact on the pages that I read. It does not mean that the fact does not exist.

This list is not a review of the work of these firms. I scored public evidence only, and a sales call can fill most of the gaps.

Add the eight totals: 10, 8, 5, 4, 4, 4, 3 and 2. The sum is 40, so the average is 5.0 of 12. The leader has twice the average.

The columns say more than the rows. Two companies of eight published anything about price. One published a statement that the client owns the code. Three named a security attestation. Two described how they measure the accuracy of an agent.

That matches the result of our earlier ranking of ten other companies. In both samples, price and code ownership were the facts that firms published least. So whoever is on your shortlist, plan to ask for both in writing.

The directory tier did not predict the total. Two of the three Preferred partners scored 4 and 3. The top score went to a Select partner.

A tier measures size and experience with Claude. It does not measure what a firm tells a buyer before the contract. Use the tier as a filter, then use the other five checks to decide.

The sample has one more pattern. Two of the eight firms have a New York address: A.Team in the directory and 28Stone Consulting on its own site. If you need a builder in the city, that is a short list to start with.

After the checks, invite two or three companies to a call. Send the same written questions to each one before the call. Compare the written answers, not the conversations.

The middle column is not a perfect answer. It is the shape of an answer that you can check later.

One answer deserves more attention than the others: the test. If your data is not sensitive, ask each finalist to run its agent on 20 of your past cases before the contract. A company that cannot do that before the contract will not find it easier after.

For the shape of a published result, see our Beyond Points case study. It reports a live run of 30 real searches before beta. The product returned ranked results for 29 of them, with a 0% error rate and a median time of 20 seconds.

The same page says what the team did not measure. It does not quote user counts, bookings or dollars saved, because the product had not opened to the public. That is the kind of limit that I want to see in any proposal.

Also read what the proposal leaves out. A proposal with no line for support after launch hides a cost. A proposal with no line for model usage hides a second cost.

I am not a lawyer, and this is not legal advice. This is the list that I give to the lawyer of a client, so that the review is short.

1. 1.A signed assignment of the code.: Name the code, the prompts, the tests and the documentation. The Copyright Act requires a signed writing for a transfer of copyright ownership. A statement on a website is not that writing.
2. 2.Access to the repository from the first week.: Ask for the code in an account that you control. Then the handover is a habit, not one event at the end.
3. 3.Clear terms for your data.: Write down who can see your data and how long they keep it. Ask each model provider the same question.
4. 4.Acceptance tied to the test.: Define acceptance as a pass mark on the agreed test set. Then both sides know when the project is complete.
5. 5.An exit.: Include a notice period, an export of the data and the logs, and a handover of the code. If the agent touches protected health information, put the business associate agreement in the same packet.

On the third term, read the policy of the model provider, not only the policy of the builder. For example, the Anthropic privacy center says that by default it does not use inputs or outputs from its commercial products to train its models. That covers its API. The same page says that explicit feedback can change that.

Ask your lawyer about the copyright of code that AI tools help to write. In its January 2025 report, the Copyright Office concluded that AI assistance in the process of creation does not bar copyright. It also said that prompts alone are not enough for protection.

Two of these terms protect you even if the project goes well. Ownership and the exit let you change builders later, with no need to start again.

The full list of ten warning signs is in the checklist for the hire of an AI agent developer. This section adds the signs that I found while I scored the eight companies. They are in the order that you meet them.

• On the website: a market statistic with no source. One site in my sample states that 95% of enterprise generative AI initiatives fail to deliver. That number is in a family of failure rates with no reliable source, and I refuse to print it as fact.
• On the website: outcome percentages with the client names withheld. A confidentiality clause can explain the missing names. It cannot make the numbers checkable.
• In the directory: a tier on the website that is different from the tier in the directory. Trust the directory.
• On the call: a team with no names. Ask who will write the code and who will answer the phone after launch.
• In the proposal: a price before the company asks what the agent must read and write.
• In the contract: a license to your own code, in place of an assignment.

The first sign needs a word of explanation. Three failure rates travel widely in this market: 95% of pilots, 85% of projects and 87% that never reach production. I refuse all three, and I give the reasons in the build versus buy framework. A builder that repeats one of them did not check it.

The law applies to claims that vendors make to businesses, too. On May 21, 2026, the FTC announced proposed orders against Cox Media Group and two other firms. It said that they told small business customers that a special algorithm listened to conversations from smart devices.

The FTC said that the service did not listen to conversations or use voice data at all. The three firms agreed to pay a total of $930,000. On August 27, 2026, the FTC announced that the Commission voted 2 to 0 to approve the final orders.

DLA Piper counted the pattern. Of the last eight such FTC cases, seven involved marketing claims made to other businesses.

The rule is older than these cases. In September 2024, the FTC announced five actions under Operation AI Comply. Its Chair said that there is "no AI exemption from the laws on the books."

This is a scenario, not a client. The inputs are plausible, so that you can replace them with your own.

Consider an insurance agency in New York with 40 people. It wants an agent that prepares renewal packets from its agency system and its email. A person reviews each packet before it goes to the client.

Step 1: the three conditions. Assume that no packaged agent reaches the agency system. Then condition one is true, and a company is necessary.

Step 2: the shortlist. The agency selects four names: two from the directory, one New York specialist and one small agency. Six checks at ten minutes each is one hour for each name. Four names take four hours.

Step 3: the written questions. Eight questions to four companies give 32 answers. The agency reads them in one afternoon. Then it invites two companies to a paid test.

Step 4: the test set. The agency collects 150 past renewals and writes down the correct packet for each one. It sets a pass mark of 90% before any run. At 150 cases, 90% allows 15 failures.

Step 5: the budget. Suppose that one finalist is us. Our price is $5,000 plus a $5,000 setup fee, so $10,000 to start. After launch, it is $2,500 a month. Year one is $40,000 plus usage, and each later year is $30,000 plus usage.

Check the year one figure: $10,000 plus 12 months at $2,500 is $40,000. Three years cost $40,000 plus $30,000 plus $30,000, which is $100,000 before usage.

Now compare the A.Team figure for one senior builder, roughly $25,000 a month. Three months of one builder is $75,000. The two numbers buy different things, so compare the scope, not only the total.

The agency now has four facts for each finalist: the six checks, the written answers, the test result and the cost for three years. That is enough to decide. In this scenario, the work takes about one week of part-time effort.

Is your company in New York? Our team for AI agent development in New York can help with the shortlist and the test set.

This section is an advertisement, and I label it that way. Frenchy Digital is my company. I did not score it in the list above, because a company cannot score itself fairly.

Where we fit: one agent, a fixed budget and direct access to me. Where we do not fit: a program across many countries, or a procurement process that requires an attestation.

On the six checks, judge us yourself. Our price is on this page. Ask us for the ownership clause and the test plan in writing, the same as for any other company.

To talk about your shortlist, book a discovery call. If a packaged agent or a different builder fits better, I will say so.

This is what I did not verify, or decided not to use as evidence.

• I scored eight companies from one vendor directory. Firms that work mostly with other models do not appear in it.
• I read each site on November 11, 2026, and only a few pages of each site. A zero means that I did not find the fact on those pages.
• The directory page shows only a preview of each tier. I could not read the full lists of 42 Preferred and 295 Select partners.
• I did not check review sites today, so no entry shows a rating.
• I could not open the Gartner and OpenAI pages that I planned to cite, because they refused automated access. I used no facts from them.
• Every outcome percentage on the sites of the companies is a claim of the company. I did not test any of them.
• The worked example is a scenario with assumed inputs, not a client.
• I am not a lawyer. The legal points are a list for your lawyer, not advice.

One fact gives context to all of this. The Census Bureau put AI use at 19.8% of U.S. businesses as of May 3, 2026. The market for builders is young, and public evidence is still thin. Expect the scores in this guide to change as firms publish more.

1. 1.Write down the task and check the three conditions. If none is true, test a packaged agent first.
2. 2.Select four names. Run the six checks on each name and write down the score.
3. 3.Send the eight written questions to the top two or three. If your data allows it, ask each one to run on 20 past cases.

Then compare the written answers side by side. The difference between the companies is easier to see on paper than on a call.

Start with the task.