AI Agent Development Services: A Buyer's Guide

An AI agent is software that takes multi-step action toward a goal: it reads a request, decides what to do next, calls tools or APIs to do it, checks the result, and decides again, rather than answering a single prompt and stopping. An agent development engagement builds one against your own data and your own systems, shipped into your repository.

## Key numbers

- **50+ projects shipped**, built across 74 technologies, per our track record.
- Builder Pod **$5,000/month**, Growth Pod **$10,000/month**, Enterprise custom, all month-to-month with 30 days' cancellation notice.
- Pods start work within **5 business days**; first shipped output lands in week one or two.
- Small agent builds run **1-3 months**; medium ones **3-12 months**; past a year is rare.
- Two HIPAA-aligned platforms shipped to date, both under a signed Business Associate Agreement, not a certification (HIPAA has no certification to hold).

## What "AI agent" means, concretely

A single-turn AI feature answers a question or drafts a paragraph and stops. An agent does more: it holds a goal, breaks it into steps, calls tools (a database query, an API, a document lookup, a write action against a system of record), reads what comes back, and decides the next step, sometimes for dozens of steps in a row before it stops or asks for help.

The distinction matters because the engineering is different. A chat feature needs a prompt and a model call. An agent needs an orchestration layer (what decides the next action), a tool interface (what the agent is allowed to call and how), state (what it remembers across steps), and a stopping condition (when it hands back to a human or declares the task done). Get any of those four wrong and the agent either does nothing useful or does something wrong with confidence.

Concrete examples from work we have shipped, described without a client name: a voice-to-chart pipeline that takes a dictated clinical note, drafts a structured entry, and a vision model reads an attached radiograph, both landing in the patient chart automatically. A fraud-detection engine that ingests accounts-payable data, runs eight detectors, scores findings, and produces a per-jurisdiction PDF briefing, no human touching the pipeline until the output lands. A compliance scanner that crawls a site, extracts the privacy policy with a headless browser, grades it against a rubric, and returns specific fixes, running on a schedule with no analyst reading the policy by hand. Each of those is a chain of decisions and tool calls toward a defined outcome. None of them is a single prompt.

## Agent development service vs. buying an agent builder or platform

A no-code agent builder or platform gives you a canvas, a library of pre-built connectors, and a hosted runtime you don't control. It is the right tool when your workflow maps cleanly onto the connectors the platform already ships, your data can live wherever the platform stores it, and you're comfortable with the platform's guardrails, rate limits, and pricing as your usage grows.

An agent development service is different on four axes:

**Tool integrations.** A platform gives you what it has already built. A development engagement builds the connector to your actual system, whatever that system is, including the legacy database, the internal API with no public documentation, or the EHR with a schema nobody outside your team fully understands.

**Your data.** A platform typically processes your data on its infrastructure, under its data-handling terms. A development engagement ships into your cloud account and your database from week one. Your data does not have to leave your environment to make the agent work.

**Your guardrails.** A platform ships default guardrails that apply to every customer on it. A development engagement builds the specific check your workflow needs: never write to the billing system without a second confirmation, never message a patient outside business hours, never approve a payment above a threshold without a human sign-off.

**Deployed in your infrastructure.** When the engagement ends, the agent keeps running because it lives in your repository and your cloud account, not on a vendor's servers you're paying to keep the lights on. If we disappeared tomorrow, nothing in the system calls an Asaasin-only service.

The tradeoff is time and cost per unit of customization. A platform is faster to a demo. A development engagement is faster to a system that does exactly your workflow, with your data, under your guardrails, and stays yours.

## How the engagement actually runs

The process is the same one used for any staff-augmentation build, applied to an agent instead of a feature:

1. **We meet once.** 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.
2. **We build a free prototype.** A clickable version of the agent's core loop, built before you commit to anything. You keep it either way.
3. **The pod starts.** A pod lead and senior engineers work in your repository and your cloud account from day one, not a sandbox.
4. **Daily standups, weekly ships.** Progress lands in your existing Slack, Teams, or email thread. Working code ships every week, not at the end of a fixed-bid milestone.
5. **Handover.** Repository, migrations, deploy pipeline, and documentation, all in your accounts, at the end.

Full detail on the cadence and the standup structure lives on the [how it works](/how-it-works) page. Most pods are working within five business days of that first session, and the first shipped agent behavior lands in week one or two, not month three.

The architecture underneath any agent worth shipping looks roughly like this:


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Every box in that diagram is something we name and own on your pull request: the orchestration logic, the tool interfaces, the guardrail checks, and the audit log all live in your repository, reviewed by the named engineer who wrote them, with tests in CI. AI-assisted code goes through the same review gate as any other code we ship. We do not train models on your data.

## The eval question every vendor should have to answer

Any vendor can demo an agent that does the right thing once, on camera, with a clean input. The question that separates a real engagement 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?**

A vendor who cannot answer this concretely is selling you a prototype, not a system. Push for specifics on three things:

**The eval itself.** What is the test set the agent is checked against, and how often does it run? Is there a held-out set of real cases where the correct outcome is known, and does the agent get scored against it before every deploy, or only at the demo?

**The guardrail.** What is the agent explicitly not allowed to do without a check: write to a system of record, send a message to a customer, approve a transaction above a threshold? A guardrail is a rule enforced in code, checked before the action executes, not a note in a prompt asking the model to be careful.

**The human-in-the-loop and rollback path.** When the agent's confidence is low or the guardrail trips, what happens? A well-built agent hands off to a person with the context they need to decide fast, and every action it does take is reversible or at minimum logged with enough detail to audit and undo. If a vendor's answer to "what happens when it's wrong" is "it usually isn't," that is not an answer.

This is also where the difference between a pilot and a production system usually breaks. A pilot proves the model can do the task on a good day. Production means the eval, the guardrail, and the rollback path exist as code, not as intentions, and someone on the build owns them by name.

## What an agent development engagement costs

We run agent builds the same way we run any staff-augmentation build: as a pod, priced by capacity, not by the hour.

| Pod | Price | Build tracks | Team |
|---|---|---|---|
| Builder | $5,000/month | 1 active track | Pod lead + 2-engineer bench |
| Growth | $10,000/month | 2 concurrent tracks | Pod lead + 3-engineer bench |
| Enterprise | Custom | 3+ parallel tracks | Dedicated senior lead + 3-8 engineers |

Every tier is month-to-month with a 30-day cancellation notice by email. There is no per-hour billing, no statement of work per feature, and no change order when the scope shifts, which it usually does once the first version of an agent meets real data. Full detail on what each tier includes, including the strategy calls and architecture planning that come in at Growth and above, is on the [pricing page](/pricing) and the [pods page](/pods).

A single-track agent build (one workflow, a handful of tool integrations, an eval and guardrail layer) fits the **Builder Pod** for most first engagements. A build that needs two workflows running in parallel, or a workflow plus the internal dashboard that lets a human review the edge cases, fits **Growth**. An organization automating agent workflows across multiple departments at once, each with its own compliance and access requirements, is an **Enterprise Organization Pod** conversation.

For comparison against hiring an in-house AI engineer directly, a loaded US senior engineer runs roughly $250,000 or more a year once salary, benefits, and recruiting are counted; that is an estimate, and it moves with seniority, region, and how competitive your local market is for the skill set. A Builder Pod at $5,000 a month is roughly $60,000 a year for a lead plus a two-engineer bench, sized to one build track, which is a different shape of spend than one hire carrying the whole thing alone.

## When an agent build fits, and when it does not

An agent is the right shape for a workflow when three things are true: the task involves multiple steps and at least one decision point, it calls tools or systems that already exist (a database, an API, an internal tool), 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.

An agent is the wrong shape when the task is genuinely single-turn (summarize this document, answer this question), when the workflow has no clear stopping condition or success criteria (nobody can say what "did it correctly" means), or when the cost of a mistake is high and unrecoverable and no guardrail can catch it before damage is done. In those cases, a well-scoped single-call AI feature, or a human process with AI assistance rather than AI autonomy, is the honest recommendation. We would say so in the first session rather than build an agent that looks impressive and fails in production.

Staff augmentation through a pod is also not the right model for every buyer. If you need one senior engineer embedded on your team for an open-ended period with no defined build track, direct hiring or a contractor relationship may fit better than a pod; our [staff augmentation guide](/blog/what-is-staff-augmentation) covers that distinction directly. If what you actually need is architectural leadership and technical strategy rather than hands writing code, a [fractional CTO engagement](/blog/fractional-cto-services) is the better starting point. A pod is built for a defined build track shipped weekly, not a headcount replacement.

## A checklist for evaluating an AI agent development vendor

Before signing anything, ask a vendor these directly and expect specific, non-evasive answers:

- What tools and systems will the agent actually call, and who builds those integrations, us or you?
- Where does our data live during and after the build: your infrastructure or ours?
- What is the eval set, and how often does the agent get scored against it before a deploy ships?
- What is the guardrail for the highest-risk action this agent can take, and is it enforced in code or in a prompt?
- What happens when the agent is uncertain: does it hand off to a human, and with what context?
- Is every agent action logged in a way we can audit after the fact?
- Do we own the repository, the model calls, and the deploy pipeline from day one, or only at project end?
- What is the cancellation notice, and is there a penalty for pausing or stopping?
- If your company disappeared tomorrow, does the agent keep running?
- How many comparable systems have you shipped, and can you describe one without naming the client?

That last one is worth pressing on. A vendor who has built one agent once and is selling you the second one should say so. Track record matters here specifically because agent failures compound quietly until someone checks the audit log, and a vendor who has been through that failure mode before builds the guardrail before you ask for it.

If it helps to see how other vendors in this category position themselves, our [comparison of AI agent development companies](/blog/top-ai-agent-development-companies) covers the field, and our [AI automation services guide](/blog/ai-automation-services) walks through which workflows are worth automating first versus which ones are better left as-is for now.

## The short version

An AI agent is software that takes multi-step action toward a goal by calling tools and making decisions, not a chatbot that answers one prompt and stops. A development engagement builds that agent against your own data, your own tools, and your own guardrails, deployed into your infrastructure from day one, which is the real dividing line against a no-code agent builder. Before signing with any vendor, make them answer the eval question directly: how they measure whether the agent did the right thing, and what happens the moment it does not. Pricing runs $5,000 a month for a single-track Builder Pod up to a custom Enterprise Organization Pod for multi-department builds, all month-to-month, with work typically starting inside five business days.