AI Agent Development Company in Dallas

Agent budgets in DFW move with autonomy boundaries and integration surface, not with headcount, so the shape of the work is the more useful answer. A scoped single-purpose agent (one workflow, one or two tools, one user channel, written eval set) is a ten to fourteen week engagement. A multi-tool internal-operations agent integrated with Salesforce, HubSpot, ServiceNow, or Microsoft 365 typically runs eighteen to twenty-eight weeks including evaluation, guardrails, observability, identity (Okta or Entra ID), and audit logging. A regulated vertical agent (wealth management, claims, prior-authorisation) adds NAIC, HIPAA, GLBA, or SR 11-7 documentation to scope and extends the timeline further. Model inference and orchestration costs are billed at cost-pass-through after a monthly cap. We give fixed-fee proposals after a one-week paid discovery. Anthropic, OpenAI, Azure OpenAI, Bedrock, and Vertex AI contracts are typically owned by the client, not us — we deploy into their existing commercial terms.

We classify every agent action by reversibility and blast radius. Fully reversible, low blast-radius actions (drafting a reply that the user can edit and send, classifying a ticket, suggesting a meeting time, summarising a document) can be agent-autonomous with logging. Irreversible or high-blast-radius actions (sending an email to a customer, executing a trade, approving a claim, dispatching a field tech, writing to a system of record) gate on human confirmation in the early phase. As production data accumulates and the agent's accuracy on that action class exceeds a documented threshold over a documented window, we promote the action to autonomous with sampling — every Nth action still routes to human review for ongoing eval. We document this promotion criterion in writing before the agent ships, so the in-house operations lead and internal audit can defend the design. Texas TDPSA profiling rules and NAIC Model Bulletin both expect this kind of governance documentation.

Yes. The NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers (adopted December 2023, adopted by Texas) and the Texas Department of Insurance (TDI) aligned guidance require an AI Systems Program governing development, deployment, monitoring, third-party AI oversight, testing, validation, transparency, and consumer protection. Texas Insurance Code Chapter 544 unfair discrimination provisions apply to any agent making or supporting underwriting, pricing, or claims decisions. We build agent systems for Liberty Mutual DFW back-office, USAA-adjacent partners, and regional Texas carriers with documented model cards, fairness testing across protected classes, bias mitigation steps, human override workflow at every decision point, third-party AI vendor due diligence packets, and incident response procedures. The deliverable set is what TDI examiners and internal audit will accept without back-translation.

Schwab Westlake's relocation from San Francisco in 2021 plus Fisher Investments Plano expansion pulled significant RIA talent into DFW. Agents in this space handle inbound client-service triage, portfolio drift detection against IPS targets, KYC refresh workflows under FINRA Rule 2090, AML pattern detection under BSA, document classification on inbound forms (W-9, beneficiary designations, transfer-in paperwork), and advisor-facing meeting-prep briefs. SEC Reg BI documentation requirements, FINRA Rule 4530 reporting, and the NASAA model rules for state-registered advisors set the documentation bar. We integrate with Schwab Advisor Center, Fidelity Wealthscape, Pershing NetX360, Orion, Black Diamond, Tamarac, eMoney, and RightCapital depending on the firm's stack. Trade execution is human-gated by default — agents propose, advisors execute — until the firm's compliance committee documents otherwise. Audit logging captures every agent decision and every advisor override for FINRA Rule 4511 books-and-records purposes.

Agentic prompt injection is more dangerous than non-agent prompt injection because injected instructions can trigger real-world write actions. Our defence-in-depth: (1) Tool scoping — every tool has a least-privilege scope, write actions require human-in-the-loop in the early phase, and any external API call is rate-limited and cost-capped per session. (2) Trust isolation — untrusted retrieved content (web pages, customer emails, support tickets) is delimited and tagged, cannot grant tool-use permissions, and cannot escalate to system-message instructions. (3) Input and output guardrails — Lakera Guard, NeMo Guardrails, custom classifiers, Microsoft Presidio for PII, and structured output validation against Pydantic schemas. (4) Red-team — Garak, PyRIT, and custom jailbreak suites run against every production agent before launch and on every model upgrade. (5) Observability — every tool call is logged with full input/output to LangSmith, Langfuse, or self-hosted OpenTelemetry, and anomalous flows page on-call. We document residual risk in writing — agentic systems are not unjailbreakable.

Most workflows that founders frame as 'agentic' are actually deterministic workflows that need a small LLM step somewhere. If the inputs are structured, the steps are predictable, and the success criteria are programmable, build a deterministic workflow (Temporal, Airflow, Prefect, or a state machine) and call the LLM only at the steps where natural-language reasoning is genuinely needed. Build an agent when (a) the input is unstructured and the next step depends on classification the LLM is good at, (b) the workflow has branching where different tools are needed at different stages, (c) the workflow benefits from short-term memory across multiple turns with a user, or (d) the task requires planning across an open-ended action space. Even then, the agent is usually 3 to 8 tools, not 50 — over-broad tool sets explode the failure surface. We push back on agentic framing when a workflow would be more reliable and 10x cheaper as deterministic code with an LLM step.

Most production multi-agent systems are over-engineered. A single well-scoped agent with the right tools handles the workload of three loosely-coordinated agents at lower latency, lower cost, and dramatically lower failure surface. We use multi-agent architectures when (a) different agents need genuinely different model selections or fine-tuning (a routing agent on a small model, a deep-reasoning agent on Claude Opus), (b) different agents have different tool scopes that should not collapse into one super-agent for security reasons, or (c) the workflow has parallelisable subtasks where async coordination is faster than sequential. We use LangGraph or CrewAI for graph-based coordination, MCP for inter-agent tool exposure where it fits, and explicit message-passing schemas with Pydantic validation. We do not use vague autonomous-swarm patterns for production — they fail in ways that are hard to debug and harder to defend in an audit.

A scoped single-purpose internal agent (one workflow, two to four tools, one channel like Slack, Teams, or a custom web UI) takes twelve to eighteen weeks from kickoff to production rollout, plus four to eight weeks of evaluation tuning and broader user-base rollout. A multi-tool sales or customer-service agent integrated with CRM, ticketing, knowledge base, and identity takes sixteen to twenty-six weeks. A regulated vertical agent under NAIC, HIPAA, GLBA, or SR 11-7 governance takes twenty-four to forty weeks because the documentation and audit prep are substantial scope, not a checklist exercise. Customer-facing agents take longer than internal agents because external UX, brand voice, abuse handling, and load testing add scope. We do not promise eight-week production agent deployments to regulated DFW clients — the rebuilds we have seen from agencies that did are why we exist.