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Custom AI Agents AI Knowledge Assistant Custom Software & Apps Private AI Infrastructure All services →Custom AI agent development from Seqora turns a job your team does by judgment, over and over, into a system that does it for you. Our agents run the task, check their own output, retry when something is off, and only ask a person when they are genuinely stuck. They are built to connect to your CRM, inbox, calendar, drive and database through their APIs, and every step is logged. You own the code, the prompts and the documentation.
Custom AI agent development is building software that uses an AI model to do a specific, repeatable job that needs judgment, like reading messy documents, triaging email or drafting replies. A production agent runs the task, checks its own output against rules, retries when a check fails, and hands off to a person only when it is stuck. It connects to the tools your team already uses.
Every agent demo looks the same: prompt in, a good answer out. Then real inputs arrive. A scan is sideways, a field is blank, an email thread has three people asking for different things, and the agent confidently gets it wrong.
A smarter prompt doesn't fix that. The loop around the model does: a check on every output, retries with a reason, rules for when a person has to look, and a log of what happened. That loop is most of what we build.
Often the honest answer is that you don't need an agent at all. If the rule fits in one sentence, a plain workflow costs almost nothing to run and never makes things up. If nobody can see the number, you need a dashboard.
Two jobs stay human. Deciding what the system is for, and the apology when it breaks. We never hand either one to a model.
Clips cut in about a minute from a 4-hour-52-minute livestream, after about 3.5 minutes of transcription and 2.5 of clip finding with the agent's first, quote-based clip finder
Livestream clipping agent
Average per handwritten prescription, with two vision models reading every form and doubtful fields sent to review
Dental lab pilot · 6 real forms
Model calls in that pilot run, each logged with its stage, model, cost and time
Pilot cost ledger · June 2026
These are the patterns behind the systems below, drawn from client builds, a paid pilot and our own outbound. An agent can combine two or three of them, and scoping decides which.
A scout pass proposes candidates, a hard filter written in code throws out anything that breaks your rules, and a trimmer pass tightens what's left. It's how our clipping agent picks the moments worth clipping from a multi-hour stream.
Two independent models read the same document, their answers are merged and checked against your vocabulary, and any field below the confidence threshold goes to a review panel or a person instead of being guessed.
Specialist agents that each own one step, from research to drafting to critique, feeding a queue where a person approves before anything is sent. Our own outbound is built this way, and your CRM follow-up can be too.
Agents that read and write in your tools through their APIs. We've built on Gmail, Google Drive, OneDrive, ClickUp and Supabase, and CRMs like HubSpot or Salesforce connect the same way. Access is limited to what the job needs.
Every output is checked against a schema, your business rules and a confidence threshold. A failed check sends the agent back with the reason, and if it still can't pass, a person gets the case with full context.
Every run and model call is logged with its cost and time, so you can trace what the agent did and what it spent. At the end you get the code, the prompts and the documentation.
A first agent for a job we haven't built before is planned as an eight-week build, from kickoff to running on its own. Agents with an already-defined scope, like our financial and team management agents, are planned shorter. Larger multi-agent systems are built in phases, so each piece is useful before the next one starts.
We walk through the job with the person who does it today and sort every step into agent, workflow or dashboard. Only the messy, judgment-heavy steps get a model. You get a written scope that says what the agent may do on its own.
We collect real examples of the inputs and the correct outputs, and turn them into the checks the agent has to pass. This is where we set the escalation rules: when it retries, and when it stops and asks a person.
We build the agent, connect it to your tools through their APIs and run it on real inputs in a sandbox. Every model call is metered from the first run, so the cost per task is known early.
The agent works on live tasks while someone on your team reviews every output. We tighten the prompts and checks based on what they catch, and widen what it does alone only when the log supports it.
You get the code, prompts, documentation and a walkthrough for your team. We can stay on to tune it as your inputs change, or your team takes it from there.
Two client systems, one of them a paid pilot, and one we built for our own outbound. The dental lab stays unnamed.
A multi-pass Claude pipeline: the agent transcribes a multi-hour stream, works out who's speaking and maps every conversation by topic, then a scout pass proposes moments, a hard filter in code removes the ones that break the rules, and a trimmer pass cuts each one tight.
A 4-hour-52-minute stream processed end to end: transcription in about 3.5 minutes, clip finding in about 2.5 minutes, 10 clips cut in about a minute.
In a paid pilot on 6 real handwritten prescriptions, two independent vision models read each form, the readings were merged and checked against dental terminology, and any field below the confidence threshold went to a review panel or a person instead of being guessed. Every model call was logged with its cost and time.
About four minutes and under $1 of AI cost per prescription on real handwritten forms, with every model call metered.
Our own outbound is built on 10 specialist agents that source prospects, research their websites, fact-check what they find, qualify the fit, draft, critique and triage replies. It is built so nothing sends until a person approves it in a review dashboard.
Human approval on every send, warm-up sending limits, suppression lists and a weekly analyst memo.
Self-checks, retries, escalation rules and an audit trail are in the first version, not added after something breaks. That loop is the difference between an agent people rely on and one they switch off.
If a rule fits in one sentence, it becomes plain code. For AST Defeasance, AI reads the borrower documents, but a rule engine decides beneficial ownership and writes a plain-English reason for every row.
You get the code, prompts and documentation, and the system can run on your own cloud accounts. No platform lock-in, and your data is never used to train public models.
Most of scoping is deciding where AI belongs at all. If the real problem is that nobody can see a number, the answer is neither: it's a dashboard, kept current by a workflow.
| Compare | Plain workflow | Seqora custom AI agent |
|---|---|---|
| Use it when | The rule fits in one sentence | The input is messy and the decision is fuzzy but repeatable |
| Example | When a booking is made, send the confirmation text | Read a 40-page contract for the two clauses that matter |
| Cost to run | Close to free, runs in milliseconds | Model usage per task, metered on every call |
| When it's wrong | It repeats the same mistake until someone changes the rule | It checks its own output, retries, and escalates to a person |
| What you get | An automation that never guesses | An agent with checks, retries, escalation rules and a full log |
Two models read each handwritten lab prescription, the readings are checked against dental terminology, and any doubtful field goes to a person instead of being guessed. The full intake-to-draft pipeline is our AI document processing service.
DentalAn agent that turns a rambling intake call or web inquiry into a clean intake record, with any field it can't fill sent back to your intake team instead of guessed. Medical-records summaries run through our document processing pipeline.
PI lawAn agent that reads a stream's transcript, speaker turns and topic map, then picks and trims clips against a profile of what works on the channel, with a report on each pick. For creators it's packaged as our AI video editing agent.
Custom agents are fixed-scope builds, priced after the free scoping call. These are the things that move the number:
You see running costs before you scale. Every model call is logged with its stage, model, cost and time from the first sandbox run, so the pilot gives you a cost per task at your real volume.
It depends on the job, so we quote a fixed-scope build after a free scoping call. The main drivers are how many steps need judgment, how many tools the agent connects to and how messy the inputs are. Running costs are model usage and hosting, metered per call, so the pilot shows your cost per task before you scale.
A first agent for a new job is planned at about eight weeks: one week of scoping, one week defining what a good result looks like, two weeks of building, two weeks of supervised pilot and two weeks of handover. From you we need someone who knows the job, a batch of real examples, API access to the tools involved, and a reviewer during the pilot.
If the rule fits in one sentence, use a workflow: it costs almost nothing to run and never makes things up. Use an agent when the input is messy and the decision is fuzzy but repeatable, like reading a long contract for the two clauses that matter. If nobody can see the number, you need a dashboard. The full reasoning is in our post on where AI belongs.
It's built to catch itself first. Each output is checked against rules and a confidence threshold, a failed check triggers a retry with the reason, and anything still uncertain goes to a person with the context instead of being guessed. If something breaks while we support it, a person from Seqora calls you, explains what happened and owns the fix. That call is never automated.
You do. You own the code, the prompts and the documentation, with no per-seat license to a Seqora platform. Your data is encrypted in transit and at rest, never used to train public models, and can stay on your own cloud accounts. When the work touches patient data, we sign a BAA. See private AI infrastructure.
Anything with an API or a dependable export. We've built agents and pipelines on Google Drive, Sheets and Forms, Gmail, Microsoft OneDrive, ClickUp and Supabase, and the same API approach works for CRMs like HubSpot and Salesforce, plus Google Calendar, Slack, Notion and Monday. For web tools with no API, a browser-automation step can do the clicking.
Further reading: AI agent, workflow, or dashboard: how to decide where AI actually belongs
In one call we'll tell you whether it needs an agent, a workflow, a dashboard or nothing at all, and what the agent would check before it asks for help.