AI Agent Development Company in New York

What a New York agent engagement costs turns on how much of the org chart the agent is expected to absorb. A scoped AI agent proof of concept at NYC rates is a demonstration. A custom production agent (KYC and AML triage, AML alert review, equity research drafting, claims FNOL, prior-authorisation, campaign-workflow automation) is a working system. Enterprise multi-agent platforms with role-specialised agents, shared memory, custom evaluators, and integration into core banking systems, Epic, Salesforce Financial Services Cloud, or Adobe Experience Manager are a programme. Scope drivers include a single agent with three to five tools, an evaluation harness, an action-log dashboard, and a hosted demo with SSO. NYC rates carry a 25 to 40 percent premium over Austin or Toronto. We quote fixed fee against a signed SOW.

On October 16 2024 NYDFS issued an Industry Letter titled Cybersecurity Risks Arising from Artificial Intelligence, addressed to every Part 500 covered entity. It clarifies that AI-specific risks (deepfake-enabled social engineering, AI-augmented phishing, training data theft, prompt injection, model poisoning, third-party AI vendor risk, and biometric authentication compromise) fall under existing Part 500 obligations rather than waiting for new rules. Practical requirements: AI risk in the annual risk assessment, AI training in the cybersecurity awareness program, MFA designed to resist deepfake voice and video, vendor due diligence on AI suppliers, and incident response playbooks that include AI-specific scenarios. Our agent builds ship with an October 2024 Letter control map, a deepfake-resistant MFA recommendation for any agent that authenticates a customer (call-back verification, knowledge-based authentication retired in favour of cryptographic tokens), and an AI-incident playbook attached to the existing Part 500 72-hour reporting flow.

NYC Local Law 144 took effect July 5 2023 and applies to every Automated Employment Decision Tool used to screen candidates or employees for positions in NYC. Agentic systems that triage applicants, score open-ended responses, schedule interviews based on AI ranking, recommend promotions, or summarise candidate fit from resumes almost always fall inside the definition once they influence a hire-or-no-hire signal. We commission an independent bias audit during discovery with BABL AI, Holistic AI, or Eticas, publish the summary on the careers page, wire the 10-business-day candidate notification into the recruiting workflow, and build disparate-impact telemetry (selection rates and impact ratios across race and sex categories per the EEOC four-fifths rule) directly into the agent's evaluation harness so the annual re-audit is a query rather than a fresh project. We also layer EEOC oversight for ADA and Title VII compliance and OSHA review for any workplace-safety implications of agent-driven scheduling. Fines start at USD 500 per violation and stack daily.

Yes. The SEC's July 2023 proposed rule on conflicts of interest in predictive data analytics signaled regulatory direction, the SEC Marketing Rule 206(4)-1 already governs adviser communications, and FINRA Rule 3110 supervision plus Regulatory Notice 24-09 confirms that AI-generated client communications fall inside member firm supervision. For any agent that drafts or co-drafts client-facing content (broker copilots, RIA assistants, wealth-advisor agents), we build a supervision sample queue that scores every generated communication for suitability, fair-balance disclosure, and prohibited claims, routes a configurable percentage to a designated supervisor for review, and stores everything for the SEA Rule 17a-4 books-and-records retention period of three to six years on a WORM-compliant medium. Disclosure language about AI assistance is wired into outbound messaging. We have shipped this pattern for NYC broker-dealers and RIAs.

Every state-changing action in an NYC 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 movement of funds, any submission to a regulator or core banking system, any external customer communication, any clinical order, and any HR decision. Approvers see a structured payload (agent intent, supporting evidence, predicted impact, policy citations) and a one-click approve or reject UX. The action log captures the approver identity, the timestamp, the supporting context, and the final outcome. Internal audit at a money-center bank reviews this log during routine exams.

Clinical workflow agents at NYC academic medical centers ship inside a tight HIPAA, NY SHIELD Act, and IRB perimeter. We build prior-authorisation agents that draft appeal letters against payer policies with citation back to clinical-trial evidence and chart documentation, intake agents that triage patient symptoms against clinical decision-support rules with explicit clinician override, scheduling agents that coordinate across Epic and external referral systems, and tumor-board prep agents that summarise patient history, imaging, and pathology for the meeting. PHI stays inside AWS us-east-1 under a signed BAA, every model inference is logged for audit, and every output ships with clinician-facing explainability and citation back to the source documentation. We coordinate with the institution's IRB and AI governance committee for the QA cycle. The agent never closes a clinical decision without an attending physician on the action gate.

Agent security expands the OWASP LLM01 surface because the model now has tools that can write to systems of record. Our defence stack runs at five layers. First, input sanitisation strips and flags injection patterns in user prompts and retrieved documents. Second, retrieval-side filtering quarantines instruction-like content in any retrieved chunk before it reaches the model context. Third, a guardrail model (NeMo Guardrails, Lakera Guard, or Llama Guard 3) classifies every prompt and response. Fourth, tool-use sits behind explicit allow-lists with policy gates on every state-changing call. Fifth, agent action plans are reviewed by a separate planning-validation model before any tool sequence executes, which catches attempts to chain low-risk reads into a high-risk write. We run continuous red-teaming using PyRIT, Garak, and a custom scenario pack of known NYDFS-relevant attacks against every release.

A production AI agent for an NYC enterprise typically takes sixteen to twenty-eight weeks from kickoff. Weeks 1 to 4 are discovery: NYDFS Part 500 plus October 2024 AI Letter posture review, Local Law 144 scoping if HR is in play, SR 11-7 risk tiering, tool-use design with policy gates, and an evaluation rubric agreed with model risk. Weeks 5 to 14 are build: orchestration on LangGraph, CrewAI, or AutoGen, MCP tool servers, retrieval pipeline, guardrail stack, action-log and human-in-the-loop console, and SSO and audit-log integration. Weeks 15 to 20 are validation: red-team via PyRIT and Garak, shadow-mode against historical workload, challenger-model testing, model risk and internal audit review, and a closed pilot with one business unit. Weeks 21 to 28 are rollout, FINRA Rule 3110 or HIPAA supervision wiring, kill-switch testing, and a documented retraining schedule. We have shipped to this cadence inside NYDFS-regulated and HIPAA-covered stacks.