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AI Agent Developer: Austin
Austin's SaaS market, from the companies near the Oracle Austin campus to Dell-adjacent software startups and the broader tech ecosystem on North Lamar — is shipping agent features inside their products. Agents that paying customers use directly.
That architecture is different from an internal automation tool. Multi-tenant isolation means each customer's agent cannot read another's data. Usage metering means agent runs are billed per plan tier. Rate limiting means a single tenant cannot saturate the system. And the reliability bar is higher: a 70% success rate is acceptable for an internal tool. For a customer-facing feature, it generates churn.
We build these systems on a fixed scope, priced to the work.
Tell us about the agent feature you're shipping.
The pattern repeats across Austin's B2B SaaS market: a product team adds an AI assistant or automation agent as a paid feature tier. The MVP gets demo'd at SxSW or ships as a beta. Then production reality hits.
The internal prototype that worked fine in testing fails when 500 customers use it simultaneously. Data from customer A bleeds into customer B's context. The agent burns through API quota without anyone knowing which tenant consumed it. A single customer with high volume degrades the experience for everyone else.
These are not edge cases. They are the predictable failure modes of shipping an agent feature without building the multi-tenant infrastructure first. We have built this infrastructure. The tenant isolation layer, the metering table, the per-plan rate limiter, and the tool configuration store are all components we deliver as part of the agent build.
What customers in Austin's B2B market are paying for.
Agents that execute multi-step workflows on behalf of end users. Connect to the user's integrated apps, complete sequences like CRM data entry or report generation, and return results.
Agents that take a topic or question, query connected data sources, and return a structured summary. Common in sales intelligence, competitive research, and due diligence tools.
Agents that ingest unstructured documents, extract structured fields, and write results to the product database. Common in HR tech, legal tech, and financial software.
Agents that handle routine customer requests end-to-end: account lookups, status updates, configuration changes. Escalate to a human when outside their defined scope.
Tenant ID enforcement at the tool execution layer. Namespaced vector stores per tenant. Database row-level security policies. The agent cannot read outside its tenant boundary.
Every agent run logs token consumption, tool call count, and total cost to a metering table keyed by tenant and timestamp. Queryable for billing, cost analysis, and plan limit enforcement.
Per-tenant rate limits configured by plan tier. Free tier gets N runs per day. Pro tier gets M. Enterprise gets configurable limits. Enforced at the API layer before the agent executes.
Each tenant configures their own tool integrations: their API keys, their connected apps, their preferences. Stored in a per-tenant configuration record. Agent loads it at runtime.
Tell us what the agent does, how many tenants will use it, and what your current reliability problems are. We'll reply within one business day.