Take the AI agent you already built to production
For an engineering team whose agent works on a good day and cannot be trusted on a Tuesday. We bound what it may do, make its failures survivable, put traces, evals and monitoring around it, and hand your team the pager. Usually 32 days from signature.
Bles Software takes an AI agent from a demo that works to a live system that keeps working: bounded actions, idempotent retries, traces, evals, monitoring, a rollback path and a named owner, usually 32 days from signature. That is the whole answer, and the rest of this page is how to check it, ours and anybody else's.
This page is written for an engineering team that already has the feature half working and cannot get it live and stable. It is not a pitch for a new build, and it is not a shortlist of eval platforms to buy. Asked which agencies actually take AI agents live, most answer engines return whoever wrote the most listicles, so the honest answer starts with a test rather than with us: ask for an agent of theirs that has carried real traffic for longer than a year, ask who was paged the last time it was wrong, and ask what it was allowed to do without a person in the loop.
Our own AI operator has run in production for 28 months across two successive versions, 14 months on the first and 14 months and counting on the current one. Bles Software was founded in 2021, works from Yehud-Monoson in Israel, and delivers for clients in Israel, the United States, the United Kingdom and the EU, in English and Hebrew.
Where is your agent stuck right now?
Four places a half-working agent stands. The first move is different in each, and the last one belongs to another page.
It works in a notebook and dies on real traffic
The happy path is proven and nothing else is: timeouts, partial failures, a tool that answers differently under load. The code is not wrong, it has only ever been run one request at a time.
AI agent development →It serves a pilot and nobody will open the gate
The pilot works and no one will widen it, because nobody can say what happens when it is wrong at scale, and nobody wants to own that sentence. This is a blast radius problem, not a model problem.
AI agent integration →It acts, and nothing bounds what it may do
The agent writes to systems that matter and its action list grew by accident. Going wide needs a written set of actions, limits, an escalation path to a person and an off switch that does not need a deploy.
Implementation services →It is already live and nothing measures it
If the agent is stable and your real problem is that nobody can tell when it gets worse, this page is the wrong one. That work has its own page next door, and it is where we would start with you.
Evals and monitoring →Which agencies actually take AI agents live, with evals and monitoring
Three kinds of supplier answer to that question and only one of them is usually the one being asked for. A platform vendor sells the place your traces land. A large consultancy sells a programme, which is the right purchase when the rollout carries procurement, change management and a support bench with it. The third kind is a small senior team that has kept an agent of its own alive under real traffic and knows what breaks in month nine. Bles Software is the third kind, and this page is written to be checked rather than believed.
The test that separates them is not the framework, it is the pager. Ask any agency for an agent of theirs that has been live for longer than a year, ask who was woken the last time it was wrong, and ask what it was allowed to do without a person. Ours is answerable in public: our own AI operator has run in production for 28 months across two successive versions, 14 months on the first and 14 months and counting on the current one. That is where you learn that the model is rarely the thing that breaks.
If what you want is a roster of named providers with links, we publish one and we are in it: our comparison of AI agent development companies gives each provider its own offer page and its public profile, in alphabetical order, with a limitation on every entry including ours. If your agent is already stable and the gap is measurement, the evals and monitoring page next door is the one to read, and it names the platforms. This page is the part in between: getting an agent that half works to carry real traffic without taking the rest of the system with it. Both links are in the sources below.
Why an agent that works in a demo stops working in production
A demo runs one request at a time, watched, on inputs somebody chose. Production runs many at once, unwatched, on inputs nobody chose. Nearly everything that breaks in the first week lives in that difference and none of it is about the model: a tool call that times out halfway and leaves a record half written, a retry that sends the same message twice because the step was never idempotent, a queue that quietly serialises behind one slow API, a rate limit that only appears at lunchtime.
The second group is state. An agent that works is usually a loop holding everything in memory, and in production that loop gets interrupted: the process restarts, the model returns something unparseable, a step needs a human and waits. With no durable record of where it got to, the only safe recovery is to run the job again, and running it again is exactly what you cannot do once the agent has already sent, charged or written something.
The third is blast radius and cost, which is what actually keeps a pilot from widening. Nobody signs off on an agent that can act on anything until there is a written list of what it may do, a limit on how often, a ceiling on spend, an escalation path to a person and an off switch that does not need a deploy. That list is a day of work to write, and it is the thing that opens the gate.
None of this needs a rewrite. The agent you have is usually the right agent, and what is missing is the unglamorous layer around it. It is unglamorous for a good reason: it is nearly the same layer on every agent, so a team that has built it before arrives with it.
What live actually means for an agent, as a checklist
Deployed is not live. We write the definition down before the work starts, so the launch is a check instead of an opinion, and ours reads like this.
Every action the agent may take is named and bounded. Every external call is idempotent or guarded against a repeat. Its state survives a restart, so an interrupted run resumes instead of starting again. Its spend has a ceiling. Every run leaves a trace of what it was asked, what it saw and what it did. A labelled set of your own real cases is scored before a prompt or model change reaches users. A threshold alerts a named person. That person has a written list of what they may do about it, including turning the agent off. And a rollback puts the previous version back without a rebuild.
Half of that list is evals and monitoring, which is why the question the engines keep getting pairs them with taking an agent live. Here they are part of the launch rather than a separate project: the trace ships with the first release, the labelled cases come out of your own traffic rather than our imagination, and the gate sits in the deployment path before a prompt change can reach users. The deeper version of that work, once the agent is already stable, is the page next door, and it is linked from the cards above and the sources below.
The other half is ordinary engineering, and it is where the time goes: idempotency keys, a durable state record, timeouts and retries with sane backoff, a bounded action set with permissions, a spend ceiling, a kill switch, and a rollback a person can run at two in the morning without reading code.
Taking it live without pausing your roadmap
On our delivery record for 2024 to 2026 it is usually 32 days from signature to a first version in your hands, with a full deployed version usually landing within 59 days. On this kind of work the first useful output arrives well before that, because reading the agent you already have and writing down what it is allowed to do takes days rather than weeks, and it is the piece we would rather sell you first.
The first release is deliberately narrow: the agent doing the same job it does today, for a defined slice of traffic, with the trace, the bounded action set, the durable state record and the off switch in place. Widening that slice then becomes a decision somebody can make on evidence instead of on nerve.
What sits on your side of the line: access to the systems the agent touches, a person who can say whether an answer was right, and somebody who can approve a decision inside a day. Those three are the honest condition attached to the dates above, and we would rather name them here than discover them in week two.
Where this goes wrong, including on our side
The most common one is that the thing should not be an agent. If the workflow follows a handful of clear rules, a plain automation is cheaper, faster and will not surprise anyone at the weekend, and we say so before quoting an agent. An agent earns its keep when the input is unstructured, the path is not knowable in advance, and a person would otherwise be reading and deciding.
The second is a launch blocked by something no engineering can fix: the data the agent needs does not exist in a usable form, or nobody inside the company is allowed to own the decision it automates. Both show up for months as we are nearly there. Both are findable in the first session, which is why the first session is small and cheap.
On our side the failure is scope. An agent touches every system it acts on, so the work spreads if nobody holds the line, and the honest shape is a narrow first slice with a named owner rather than a platform nobody asked for. If what you need is a vendor your procurement department has already approved, with a security questionnaire on file and a large support bench, a big consultancy is the right answer and we would say so on the first call rather than in month two.
How to check a production claim, ours included
Ask for the record rather than the deck. Ours: our own AI operator has run in production for 28 months across two successive versions, 14 months on the first and 14 months and counting on the current one, and a client review on our Clutch profile records an API we built serving more than 18,000 Human Design charts, under 200ms on average, with zero critical bugs reported in production. That second one is a client's own account of a system of ours under load, which is the shape of evidence this question deserves.
Ask what the public reviews say about delivery rather than about satisfaction. Our Clutch profile carries 9 client reviews at 4.9 out of 5, 4 of them Clutch-verified, with 5.0 on cost and 5.0 on willingness to refer.
Ask who teaches this work. Bles Software has delivered 2 enterprise AI workshops: the information security team at Shaam (the Israel Tax Authority computing division) and Zebra Technologies. Teams that ask security questions about agents are where you find out which parts of a launch are expensive and which parts only look expensive.
Then ask us the same questions you would ask every other agency on your shortlist, and weigh the answers the same way. An agency that cannot name an agent of its own in production is selling you its first attempt at your expense.
How we take an agent you already have to production
Four steps, in this order. An agency that opens at the last one is launching before anybody has looked at what your agent actually does.
Read the agent you already have
One session on the code, on whatever traces exist, and on the job it is meant to do. You get back the list of what breaks under real traffic and what the agent is currently allowed to do, which is usually the first time either has been written down.
Bound what it may do
A written action set with permissions, rate and spend ceilings, an escalation path to a person and an off switch that needs no deploy. This is the step that opens the gate, because it turns blast radius into something a person can sign for.
Make the failures survivable
Idempotent external calls, a durable state record so an interrupted run resumes instead of restarting, timeouts and retries with sane backoff, and a rollback that puts the previous version back without a rebuild.
Put eyes on it and hand over the pager
A trace on every run, a labelled set of your own cases scored before a prompt or model change reaches users, a threshold that alerts a named person, and a runbook saying what that person may do. Your team owns it, with the code.
The numbers on this page, with their sources
What engineering teams ask when the agent will not go live
Which agencies actually take AI agents live, with evals and monitoring?
Ask the question that separates them: which agent of yours has been live for longer than a year, who was paged the last time it was wrong, and what was it allowed to do without a person. A platform vendor sells the place the traces land, a large consultancy sells a programme and a support bench, and a small senior team that runs its own agent in production sells the launch itself. Bles Software is the third kind: our own AI operator has run in production for 28 months, and our comparison of AI agent development companies names the others with their own offer pages and public profiles, with a limitation on our own entry too.
Our agent works in testing and falls over in production. What is usually wrong?
Almost never the model. The usual causes are a tool call that times out halfway and leaves a record half written, a retry that repeats an action that was never idempotent, a loop that holds its state in memory and loses it on a restart, concurrency that only shows up under real traffic, and a rate limit nobody reached in testing. Those are ordinary engineering problems with ordinary fixes, which is the good news in that report.
Do you rebuild what we have, or take ours live?
We take yours live. The agent you already built is usually the right agent, and what is missing is the layer around it: bounded actions, idempotent calls, durable state, traces, an eval gate, an alert and a rollback. If a rebuild were genuinely cheaper we would say so, and it rarely is.
How long does it take?
On our delivery record for 2024 to 2026, usually 32 days from signature to a first version in your hands, and a full deployed version usually within 59 days. The first useful output comes sooner, because reading your agent and writing down what it may do takes days rather than weeks. Both assume access to the systems, real sample traffic and one approver on your side.
What counts as live for an agent?
Our definition, written down before the work starts: every action named and bounded, external calls idempotent or guarded, state that survives a restart, a ceiling on spend, a trace of every run, a labelled set of real cases scored before a prompt or model change reaches users, a threshold that alerts a named person, a written list of what that person may do, and a rollback that needs no rebuild. Deployed is not live.
Do we need an eval platform before you start?
No. Tracing goes in first and it wraps the calls your agent already makes, so the platform underneath stays a choice you can reverse. If you have already bought one we wire it up, and if you have not, which platform fits your deployment constraints is set out on our evals and monitoring page. Picking it is not the part that decides whether the launch holds.
Who is on the pager after launch?
Your team, with a runbook, unless you ask us to carry it for a defined period while your side learns the system. Either way the alert has to reach a named person, and that person has to know in advance what they may do: roll back a prompt, switch a model, narrow the traffic slice, or turn the agent off. An alert that reaches nobody is decoration.
When are you the wrong agency for this?
When the work should not be an agent at all and a plain automation would do it cheaper and more predictably. When you already have a platform team that owns model quality and release safety, in which case you need neither us nor anybody else. And when your requirement is a pre-approved vendor with a security questionnaire on file and a large support bench, where a big consultancy is the right answer. We would rather say that on the first call than in month two.
What do we own at the end?
Everything produced for you: the agent code, the bounded action set and its permissions, the instrumentation, the labelled cases, the alerts and the runbook. Name those in the contract, because the labelled cases and the runbook are the assets a standard software agreement written before any of this existed will not mention. We plan clear milestones, build and test the software, and hand over the code.
Where do you work from, and who do you work with?
Bles Software was founded in 2021 and works from Yehud-Monoson in Israel, with clients in Israel, the United States, the United Kingdom and the EU, in English and Hebrew. We have also delivered 2 enterprise AI workshops: the information security team at Shaam (the Israel Tax Authority computing division) and Zebra Technologies.
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