asaasinAI agent development
An agent is software that takes multi-step action toward a goal: it calls your tools, checks the result, and decides again. We build one against your own data and systems, starting at a $7,500-a-month Builder Pod, with a free clickable prototype before you commit to anything.
An agent is not a single prompt. It needs four things to work at all: an orchestrator that decides the next step, a tool interface that defines what it is allowed to call, state so it remembers what happened earlier in the run, and a stopping condition so it hands back to a person or declares the task done. Get one of those wrong and the agent either does nothing useful or does something wrong with confidence.
We build all four against your actual systems, not a demo environment. That means the connector to your CRM, your EHR, your internal API with no public documentation, or the legacy database nobody outside your team fully understands. Your data does not leave your environment to make the agent work, and the code lands in your own repository and cloud account from week one.
An agent is the right shape for a workflow when three things are true at once: the task has multiple steps and at least one decision point, it calls tools or systems that already exist, and the cost of a wrong action is bounded and recoverable, or a human review step is cheap enough to insert before anything irreversible happens.
Some of what we have shipped: a voice-to-chart pipeline that drafts a structured clinical note from a dictated visit while a vision model reads an attached radiograph, both landing in the patient chart automatically. A fraud-detection engine that runs eight detectors over accounts-payable data and produces a per-jurisdiction PDF briefing with no analyst touching the pipeline. A compliance scanner that crawls a site, grades the privacy policy against a rubric, and returns specific fixes on a schedule.
An agent is the wrong shape when the task is genuinely single-turn, when nobody can define what "done correctly" means, or when a mistake would be expensive and unrecoverable with no guardrail able to catch it first. In those cases the honest recommendation is a well-scoped single-call AI feature, or a human process with AI assistance instead of AI autonomy, and we will say so in the first session rather than build an agent that looks good in a demo and fails in production.
Any vendor can demo an agent that does the right thing once, on camera, with a clean input. The question that separates a real build from a demo is narrower: how do you measure whether the agent did the right thing at scale, and what happens the moment it does not.
The eval is a fixed set of representative inputs with known-good outcomes, scored automatically before every deploy, so a change either makes the system measurably better or it gets blocked. The guardrail is a rule enforced in code, not a note in a prompt asking the model to be careful: never write to the billing system without a second confirmation, never message a customer outside business hours, never approve a transaction above a threshold without a human sign-off. When the agent's confidence is low or a guardrail trips, it hands off to a person with the context to decide fast, and every action it does take is logged in enough detail to audit and undo.
AI-assisted code goes through the same review gate as anything else we ship: a pull request in your repository, reviewed by the named engineer who owns it, typed contracts, and tests in CI. We do not train models on your client data.
Agent builds run as a pod, priced by capacity rather than by the hour. A single-track build, one workflow, its tool integrations, an eval and guardrail layer, typically fits a Builder Pod at $7,500 a month with a pod lead and a two-engineer bench. A build spanning two workflows, or a workflow plus a dashboard for a human to review edge cases, usually fits a Growth Pod at $10,000 a month with a three-engineer bench and bi-weekly strategy calls. Multi-department builds with their own compliance and access requirements move to a custom Enterprise Organization Pod.
Every tier is month-to-month with a 30-day cancellation notice by email. There is no per-hour billing and no change order when scope shifts, which it usually does once the first version of an agent meets real data. A small, single-workflow agent build runs 1 to 3 months end to end; a larger build spanning multiple workflows or heavier compliance requirements runs 3 to 12 months.
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A single session to understand the workflow the agent needs to automate: what triggers it, what tools it needs to call, what "done correctly" looks like.
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A clickable version of the agent's core loop, built before you commit to anything. You keep it either way.
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A pod lead and senior engineers work in your repository and your cloud account from day one, not a sandbox.
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Progress lands in your existing Slack, Teams, or email thread. Working code ships every week, not at the end of a fixed-bid milestone.
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Repository, migrations, deploy pipeline, and documentation stay in your accounts. Cancel any time with 30 days' notice; whatever shipped is already yours.
Client names withheld. Engineering described exactly as it shipped.
A practice-management platform with AI in the clinical loop: dictate a note and a model drafts a structured SOAP entry into the chart, while a vision model reads radiographs and an imaging hub moves CBCT scans through analysis.
A vendor-spend audit engine for a public agency that runs fully offline: eight fraud detectors over an ingest, enrich, detect, score pipeline, with an air-gapped mode backed by local models making zero external calls.
A white-label compliance scanner an agency resells under its own brand: it crawls a site, extracts the privacy policy with a headless browser, grades it against a rubric with a language model, and returns specific fixes on a schedule.
A single-track agent build, one workflow, its tool integrations, and an eval and guardrail layer, typically fits a Builder Pod at $7,500 a month. A build spanning two workflows or requiring a review dashboard alongside the agent usually fits a Growth Pod at $10,000 a month. Multi-department builds move to a custom Enterprise Organization Pod, priced in a scoping meeting.
Most pods start work within 5 business days of the first planning session, and a first version of the agent's core loop typically ships in week one or two. A small, single-workflow build usually runs 1 to 3 months end to end; a larger build spanning multiple workflows or heavier compliance requirements runs 3 to 12 months.
A chatbot answers a message and stops; it has no persistent goal and typically cannot take action outside the conversation. An agent holds a goal across multiple steps, calls tools or APIs to gather information or take action, checks the result, and decides what to do next, sometimes for dozens of steps before it stops or asks for help.
Yes, that is the core difference from a no-code agent platform: we build the connector to whatever system you already run, legacy database included, rather than asking you to migrate onto a platform's supported integrations. Our builds have run across 74 technologies to date, matched to whatever stack the project already had.
We sign a Business Associate Agreement on request and build to HIPAA-aligned controls: encryption, access logging, and audit trails enforced in code, not promised in a deck. We have shipped two HIPAA-aligned platforms to date. There is no such thing as HIPAA certification since HIPAA has no certification to hold; the accurate claim is a signed BAA plus HIPAA-aligned controls.
You do. Every build ships as maintained code in your own repository and your own cloud account, reviewed by a named engineer through the same pull-request gate as any other code we write. If the engagement ends, the agent keeps running because nothing in it calls a service we alone operate.
The guardrail layer is built to catch the specific mistake before it happens, not after: a rule enforced in code that blocks the action, routes it to a human for review, and logs it. Every action the agent does take is logged in enough detail to audit and undo. If the eval suite shows the agent regressing on a case, the deploy that caused it is blocked before it ships.
Every pod is month-to-month with a 30-day cancellation notice sent by email. Whatever has shipped up to that point is already in your repository and your cloud account, so stopping does not mean losing the work.
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Thirty minutes to map your problem to a plan and a timeline. You will leave the call with scope, price, and a start date.