AI Agent Development Company in Austin

Austin pricing sits well below the coasts and slightly above the rest of Texas, and the honest answer is in ranges because scope drives everything. A scoped agent proof of concept (one agent, three to five tools, an evaluation harness, an action-log dashboard, a hosted demo behind SSO) typically runs USD 30,000 to 75,000 over six to ten weeks. A custom production agent (verification test generation for a fab, IT support triage for an enterprise IT stack, prior-authorization drafting for a health system, matching agents for an HR platform) typically runs USD 125,000 to 350,000 including guardrails, human-in-the-loop console, observability, and compliance documentation. An enterprise multi-agent platform with role-specialized agents, shared memory, custom evaluators, and integration into core systems (Epic, ServiceNow, SAP, MES) runs USD 350,000 to 900,000 and up. Austin senior engineering rates run roughly 25 to 40 percent below NYC and San Francisco equivalents, and Texas having no state income tax keeps the local talent market cheaper than California at equivalent seniority, though Tesla, Oracle, Samsung, and Apple pull a premium over San Antonio and Houston. We quote fixed fee against a signed SOW.

TRAIGA was signed as HB 149 on June 22 2025 and took effect January 1 2026, and it is deliberately narrower than the sweeping HB 1709 draft it grew from. The final statute centers on intent-based prohibited uses: AI systems developed or deployed with the intent to intentionally discriminate against protected classes, to manipulate human behavior in ways likely to cause harm, to produce or distribute child sexual abuse material or non-consensual deepfakes, to perform biometric identification without consent, or (for government) social scoring. It adds disclosure duties for state agencies interacting with consumers through AI, creates a regulatory sandbox and a Texas AI Council, and gives the Texas Attorney General exclusive enforcement with a notice-and-cure period before penalties. Crucially for buyers, aligning with the NIST AI Risk Management Framework functions as a safe-harbor defense. Our builds ship with a prohibited-use intent screening in discovery, a NIST AI RMF-aligned risk profile that doubles as safe-harbor evidence, and documentation structured to survive an AG inquiry without a scramble.

The Texas Data Privacy and Security Act took effect July 1 2024 and applies more broadly than most state privacy laws: it reaches entities that conduct business in Texas and process personal data, without the revenue or volume thresholds that exempt mid-market companies under California's CCPA, though genuine small businesses are carved out. The provisions that shape agent architecture are the sensitive-data consent requirement (precise geolocation, biometric data, health data, and children's data cannot be processed without opt-in consent), the obligation to honor universal opt-out signals such as Global Privacy Control, and data-subject rights covering access, correction, deletion, and portability. The Texas AG enforces exclusively, with a cure period that does not sunset. Practically, our discovery includes a TDPSA data-minimization review of everything the agent will ingest, consent gates before any sensitive data reaches a model context, opt-out signal handling wired into agent memory and retention, and deletion workflows that propagate into vector stores and fine-tuning corpora, not just the primary database.

Yes, and this is a core Austin pattern rather than an edge case. Samsung Austin Semiconductor, NXP, AMD, and the wider chip-design cluster treat foundry and design IP as non-exportable to hosted frontier models, so the entire agent stack runs self-hosted: Llama 3, Mistral, or Qwen weights on on-prem or private GPU clusters, orchestration on LangGraph inside an air-gapped VPC, retrieval against internal document stores, and zero outbound calls to hosted model APIs. Tool integrations against MES, SCADA-adjacent telemetry, and verification environments are read-only by default, with any write behind a mandatory human approval gate. Guardrail models and classifiers run locally as well. The trade-off is capability headroom against hosted frontier models, which we manage with fine-tuning on internal data using LoRA and QLoRA, aggressive retrieval grounding, and task decompositions that keep individual agent steps inside what a 70B-class open model handles reliably. The same pattern serves Tesla Gigafactory supplier environments where customer contracts prohibit external model calls.

Clinical workflow agents at Austin health systems ship inside a tight HIPAA and Texas-specific perimeter. We build prior-authorization agents that draft appeal letters against payer policies with citations back to chart documentation and clinical evidence, intake agents that triage patient symptoms against clinical decision-support rules with explicit clinician override, and scheduling agents that coordinate across Epic and external referral systems. PHI stays in-region under a signed BAA, every model inference is logged for audit, and every output carries clinician-facing citations back to source documentation. Texas adds a local layer: SB 1188, effective September 1 2025, imposes duties on practitioners using AI for diagnostic or treatment recommendations, including disclosure to patients, so any agent output that could influence a clinical recommendation ships with disclosure language and stays behind an attending physician on the action gate. The agent never closes a clinical decision on its own.

Every state-changing action in an Austin enterprise agent ships behind an explicit human approval gate by default, and we relax that posture only when a documented risk review supports it. We classify each tool the agent can call into four tiers: read-only (no gate), low-risk write (post-hoc review, optional pre-call gate), high-risk write (mandatory pre-call gate, single approver), and high-impact (mandatory pre-call gate, four-eyes review with two approvers). High-impact tiers include any write to a fab MES or manufacturing execution path, any clinical order, any external customer communication, any financial transaction, and any hiring or employment action at an Indeed-class platform where TRAIGA and EEOC exposure concentrates. Approvers see a structured payload (agent intent, supporting evidence, predicted impact, policy citations) with a one-click approve or reject UX. The action log captures approver identity, timestamp, supporting context, and final outcome, and it is the same log your internal audit or a Texas AG inquiry would sample.