An AI product with agents
that know when to stop and ask
Our own marketplace runs on eight role agents and a Claude-based orchestrator with provider fallback, built through nine rounds of adversarial review before anything shipped. An AI SaaS product with agents needs the same discipline: a scoped task per agent, every decision logged, and a clear, enforced handoff to a human the moment something falls outside the rules.
What it is and who needs it
An AI SaaS product with agents fits a business whose product genuinely needs autonomous or semi-autonomous task execution, not just a chatbot answering questions, and is willing to invest in the guardrails that make that safe to run unattended. It fits a team that has seen the difference between a demo where an agent looks impressive and a production system where an agent handles thousands of real cases without an expensive mistake slipping through. It is the wrong tool for a problem a simple rule-based system or a standard form already solves; agents earn their complexity only where real judgment calls are involved.
What is inside
Agent roles scoped to specific, well-defined tasks, since one agent trying to do everything is harder to guardrail and harder to debug than several agents each responsible for a clear piece of the work. An orchestrator that manages handoffs between agents and falls back to a secondary model provider if the primary one is unavailable, so a single provider outage does not take the whole product down. Every decision an agent makes logged with its reasoning, so your team can audit behavior rather than trust a black box. Hard guardrails on anything irreversible, spending, sending a message, changing stored data, checked before the action executes, not after.
How we build it
We design the agent roles and their boundaries first, in a working session where we write down exactly what each agent is allowed to decide on its own and what must go to a human, since this boundary is the actual safety mechanism, not a detail to figure out during implementation. We build an evaluation harness testing agent behavior against real and adversarial scenarios before any agent touches a live decision, the same discipline behind our own marketplace’s nine rounds of review before launch. Guardrails are built and tested specifically by trying to make the agent do something it should not, not just by trusting the happy path to work.
What to watch
The real risk in any agent system is an agent confidently taking an irreversible action it should not have, which is why every guardrail is enforced in code before an action executes, never as a prompt instruction an agent could in principle ignore or misinterpret under unusual input. The second trap is treating a strong demo as proof of production readiness, when a demo’s handful of happy-path examples says little about how an agent behaves on the thousands of messy real cases it will eventually see; the evaluation harness specifically tests against edge cases and adversarial inputs designed to find where an agent’s judgment breaks down, not just cases it was obviously going to handle well. The third risk is cost, since a poorly scoped agent can rack up a surprisingly large model usage bill on inefficient or looping behavior; we build cost monitoring and hard spending caps into the orchestration layer from day one, so a bug produces an alert, not an unexpectedly large invoice at the end of the month.
Timeline and price
| Option | Price | What it covers |
|---|---|---|
| MVP | from $9,000 | Two agent roles, basic orchestration, manual review of edge cases |
| Production | from $15,000 | Multiple agent roles with full orchestration, hard guardrails, decision logging, human handoff rules |
| Full control (handover-ready) | from $25,500 | Everything in Production plus an evaluation harness testing agent behavior, multi-tenant architecture, and 90 days of support |
Running cost after launch depends on hosting and, where relevant, model usage, typically $20 to $150 a month for a project at this scale.
What you own at the end
You own the orchestration code, every agent’s prompts and configuration, and the full decision log, under your own model provider accounts. The system is built so you can audit, adjust or retrain any agent’s behavior without needing us to explain a black box. This is the same handover standard on every product we build: no proprietary platform only we can operate, no API key or hosting account left in our name after launch, and a written document covering the architecture and the decisions behind it. A future engineer, yours or ours on a continuing basis, should be able to extend the system without having to guess why it was built the way it was.
Related
See the development service page for our full build process. This pairs with Analytics SaaS, API-as-a-product. For the engineering detail, see AI agent runtime, LLM gateway and cost control. For a real build, see ProBay: our own marketplace, AI sales agent across seven channels.
Want this built for your business? Get in touch and we will scope it with a fixed price.
FAQ
How much does an AI SaaS product with agents cost?
From $9,000 for a product with two to three scoped agent roles and an orchestrator, 7 to 12 weeks. A product with many agent roles, multi-tenant architecture and a full evaluation harness runs $15,000 to $25,000.
How do you stop an agent from doing something it should not?
Every irreversible action, spending money, sending a message, changing stored data, passes through a hard guardrail checked before it executes, not after. Anything outside the agreed rules is logged and handed to a person instead of attempted.
What is the stack?
Claude SDK or a comparable agent framework, with provider fallback to another model for reliability, Python or Node for the orchestration layer, PostgreSQL for logging every decision an agent makes.
Can we see what the agents are actually doing?
Yes. Every decision an agent makes is logged with its reasoning and outcome, so your team can audit the system's behavior rather than trusting a black box.
Who owns the AI product and its logic?
You. The orchestration code, the agent prompts and the decision logs live in your own systems, under your own model provider accounts, with no dependency on us to keep it running.