What does an agentic AI consultant in NYC do?

An agentic AI consultant designs, builds, and deploys autonomous AI agents that run real business processes end to end. In New York, that work focuses on high-volume, rule-bound operations such as claims processing, payment and settlement exceptions, KYC and onboarding checks, and back-office administration, with the governance and audit trails that regulated New York enterprises require.

What is agentic AI for the autonomous enterprise?

An autonomous enterprise is one where repeatable, rule-heavy work runs on its own and people move up to the work that needs judgment. Unlike older automation that stops when an input changes, an AI agent reads the situation, decides the next step, acts across existing systems, and escalates to a human only when needed.

Which industries does Bradsol serve in New York?

Bradsol works with New York enterprises in financial services, healthcare, insurance, real estate, financial operations, and contact centre operations, tailoring agentic AI to each sector's compliance requirements and workflows.

How does Bradsol handle compliance for AI agents in New York?

Agents run inside the enterprise's existing security perimeter with role-based access and a complete audit trail of every action. Bradsol designs deployments around New York regulatory requirements, including DFS cybersecurity rules and the SHIELD Act, and keeps a human in the loop for anything carrying financial or regulatory risk.

What is agentic AI consulting?

Agentic AI consulting is a specialized advisory and implementation service where consultants help enterprises design, build, deploy, and govern autonomous AI agents. Unlike traditional AI implementation, agentic AI consulting focuses on multi-step reasoning systems that pursue goals, make decisions, and take actions across enterprise systems without step-by-step human direction.

How is agentic AI different from traditional AI or RPA?

Traditional AI performs specific tasks like classification or prediction. RPA executes rule-based scripts on stable interfaces. Agentic AI systems reason about goals, adapt to changing environments, use tools, and orchestrate multi-step workflows autonomously. Where RPA breaks when interfaces change, agentic AI adjusts. Where traditional AI predicts, agentic AI acts.

What frameworks does Bradsol use to build agentic AI systems?

Bradsol builds enterprise agentic AI systems using LangGraph, CrewAI, AutoGen, Microsoft Copilot Studio Agents, and Salesforce Agentforce. Framework selection is based on client requirements: LangGraph for complex orchestration, CrewAI for multi-agent collaboration, AutoGen for conversational agents, Copilot Studio for Microsoft-centric enterprises, Agentforce for Salesforce ecosystems.

How does Bradsol integrate AI agents with existing enterprise systems?

Bradsol has certified partnerships with Microsoft, Salesforce, and Automation Anywhere, plus deep integration experience with SAP, Oracle, ServiceNow, Workday, and Dynamics 365. AI agents are deployed with proper API integration, authentication, audit logging, and rollback capabilities to work within existing enterprise architectures.

What industries does Bradsol serve with agentic AI?

Bradsol delivers agentic AI solutions across healthcare, financial services, manufacturing, insurance, retail, and pharmaceutical industries. Industry expertise includes HIPAA-compliant healthcare agents, KYC and fraud detection agents for financial services, predictive maintenance agents for manufacturing, and regulatory compliance agents for pharma.

How long does a typical agentic AI implementation take?

Timeline depends on scope. A single-use-case agent typically deploys in 8-12 weeks. Multi-agent enterprise systems take 4-6 months. Bradsol's approach starts with a 4-week readiness assessment followed by iterative deployment cycles, minimizing risk and validating business value at each phase.

What governance framework does Bradsol use for AI agents?

Bradsol applies a proprietary AI automation methodology covering readiness assessment, agent design, integration, deployment, and ongoing governance. Every deployment includes decision audit logs, human-in-the-loop escalation paths, model observability, and compliance checkpoints aligned with client's regulatory environment.

How is Bradsol different from Big 4 consulting firms for AI implementation?

Bradsol combines specialist depth in AI automation with the enterprise delivery experience of a larger consultancy. Clients get direct access to senior consultants, faster engagement cycles, and no bloated overhead. Bradsol has delivered 500+ automation deployments across the US, UK, UAE, and Australia.

Should we replace our existing RPA with agentic AI?

Not necessarily. The 2026 pattern is hybrid: agentic AI handles the reasoning and exception-heavy work, while stable RPA continues to execute the deterministic tasks it was designed for. Bradsol's approach identifies which processes benefit from agentic replacement and which retain existing RPA, based on maintenance cost, exception rates, and variability.

How do I start an agentic AI engagement with Bradsol?

Bradsol engagements begin with a complimentary AI Automation Readiness Assessment. This 4-week discovery phase identifies high-impact use cases, evaluates data and system readiness, and produces a prioritized roadmap. Contact Bradsol via the website form or schedule a call directly with an AI automation consultant.

What is Agentic AI consulting?

Agentic AI consulting involves designing and deploying autonomous AI systems that can sense, plan, decide, and act independently within enterprise workflows. Bradsol's consultants define strategy, select agent frameworks, and deploy production-ready autonomous systems.

What industries does Bradsol serve with Agentic AI?

Bradsol delivers Agentic AI solutions for Retail, Financial Services, Manufacturing, Healthcare, Pharma and Life Sciences, and Real Estate.

How is Agentic AI different from RPA?

Traditional RPA automates rule-based tasks following fixed scripts. Agentic AI goes beyond this — AI agents can reason, plan multi-step actions, handle unstructured data, make decisions under uncertainty, and continuously improve through learning.

Azure Devops Engineer (Agentic AI)

Notice Period: Immediate

Experience: 3+ Yrs

Key responsibilities

CI/CD and release management

Design and maintain multi-stage YAML pipelines in Azure DevOps (and GitHub Actions where clients require) for apps, APIs, data pipelines and AI agents.
Set up branching strategies, pull-request policies, environments, approvals and automated rollback.
Manage Azure Boards, Repos and Artifacts for multiple client projects.

Infrastructure as code and platform

Provision Azure environments with Terraform or Bicep: AKS, App Service, Container Apps, Azure Functions, API Management, networking and storage.
Build and secure container workflows with Docker, Azure Container Registry and Helm.
Maintain dev, test and production landing zones with consistent tagging, policies and cost controls.

AI and agent deployment (LLMOps)

Automate deployment of agents and models to Azure AI Foundry, Azure OpenAI and Databricks Model Serving, including Databricks Asset Bundles.
Version prompts, agent configs and evaluation datasets; gate releases on automated quality and safety evaluations.
Manage vector store, AI Search and model endpoint infrastructure, quotas and scaling.

Security, monitoring and reliability

Implement DevSecOps: secret management in Key Vault, managed identities, Entra ID RBAC, private endpoints, and code and container scanning.
Set up observability with Azure Monitor, Application Insights and Log Analytics, including LLM latency, token usage and cost dashboards.
Own incident response, backup and disaster recovery runbooks for production systems.

Collaboration

Work with AI, data and app engineers to make deployments repeatable across client tenants.
Document pipelines, environments and handover guides for client IT teams.

Required skills and qualifications

Bachelor’s degree in Computer Science, Engineering or a related field.
About 3 years in DevOps or cloud engineering, with at least 2 years on Azure DevOps and Azure.
Strong YAML pipeline authoring in Azure DevOps; working knowledge of GitHub Actions.
Hands-on Terraform or Bicep for Azure resources.
Docker and Kubernetes (AKS) in production, including Helm.
Scripting in PowerShell, Bash and Python.
Solid grasp of Azure networking, identity (Entra ID, managed identities) and Key Vault.
Experience with Azure Monitor, Application Insights and Log Analytics.
Exposure to deploying ML or LLM workloads (Azure OpenAI, AI Foundry, Databricks or similar).
Clear communication and comfort working with client IT teams.

Good to have

Certifications: Microsoft AZ-400 (DevOps Engineer Expert), AZ-104 or AZ-305; HashiCorp Terraform Associate.
Databricks Asset Bundles, Unity Catalog and workspace automation.
Power Platform ALM: solutions, pipelines and Copilot Studio deployment.
MLflow, prompt/version management and LLM evaluation in CI.
GitOps tooling (Argo CD or Flux) and policy-as-code (Azure Policy, OPA).
Familiarity with ISO 27001 or SOC 2 control requirements.

Key responsibilities

Required skills and qualifications

Good to have

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