Trusted By Startups, SMBs to Fortune 500 Brands
Add engineers who can turn an agent idea into a controlled production workflow—connecting models, tools, enterprise data, evaluations, guardrails, observability and accountable handoff.
16+ Years of AI, Cloud, Data & Software Delivery
250+ Engineers Across AI, Data, Cloud & Product Engineering
U.S.-Led Project Management | Global Delivery
Hire AI agent developers for a defined integration or evaluation gap, or for ownership across architecture, tools, data, behavior, deployment and improvement. Looking for managed, end-to-end delivery instead of dedicated hiring? See our custom AI agent development services. Need broader AI talent beyond agent engineering? Hire AI developers or explore our custom software development services for the surrounding application. For ongoing production operation of agents already built, see our managed AI agent services.
Translate one workflow into goals, actors, tools, decision boundaries, success measures, risk tiers and the simplest architecture that can work.
Build actions around APIs, databases, files, search, code or business systems using validated schemas, permissions, retries, idempotency and approvals.
Ground responses in approved knowledge, design retrieval and citations, manage state, and control what information can enter or leave the agent.
Use routing, handoffs or specialist agents only when separation creates measurable value; define shared state, stop conditions and failure ownership.
Build test sets, graders, traces, red-team cases, guardrails, human review, cost/latency monitoring, regression gates and incident procedures.
Deploy to the approved stack, tune model/tool choices, document controls, transfer ownership and establish a measured improvement backlog.
Our AI agent developers bring proven experience across agent architecture, tool integration and MCP, RAG and knowledge grounding, multi-agent workflows, evaluation and guardrails, and observability and deployment.
Design agent architecture with explicit routing, handoffs, shared state and stop conditions so multi-step workflows stay predictable.
Connect agents to enterprise systems using validated tool schemas, Model Context Protocol and REST/GraphQL integrations with defined permissions.
Ground agent responses in approved knowledge with retrieval, reranking, citations and controlled memory and session state.
Engineer single-agent and multi-agent workflows, choosing orchestration only where separation creates measurable value.
Build evaluation sets, guardrails, human-approval gates and red-team tests before expanding an agent's access or autonomy.
Deploy agents with traces, structured logs, cost and latency monitoring, and a documented handoff to your team.
Review a representative role profile, then request two or three current CVs matched to your workflow, model providers, agent framework, enterprise systems, retrieval needs, action risk, deployment environment, security controls, evaluation criteria and working-hour overlap.
Keep each item visibly labeled "Solution Blueprint" until DreamzTech verifies the client, production status, contribution, evidence, outcome and permission to publish.
Environment: support and CRM
Core: LLM, RAG, ticketing/CRM APIs, approvals
Ground answers in approved policy, classify requests and propose or execute permitted actions. Accept on task completion, citations, unsupported-answer rate, permissions, approval coverage, escalation and audit evidence.
Environment: enterprise documents and data
Core: search, SQL/API tools, traces, eval set
Break an investigation into bounded queries, gather evidence and produce a sourced summary without unrestricted access. Accept on known-case recall, tool accuracy, traceability, access boundaries, latency and cost.
Environment: intake, review and approval
Core: extraction, specialist agents, state, human review
Route documents through extraction, validation and domain checks while keeping consequential decisions with reviewers. Accept on field accuracy, exception routing, state transitions, approval capture and failure recovery.
Simple & Transparent Pricing | Fully Signed NDA | Code Security | Easy Exit Policy
Begin with the job the agent must complete, the systems it may read or change, the evidence users need and the actions requiring approval. Share representative inputs, failures and the current architecture.
Share your agent workflow and requirements and we will design the fastest path to a controlled, production-ready deployment.
Our AI agent developers bring proven experience across agent architecture, tool integration and MCP, RAG and knowledge grounding, multi-agent workflows, evaluation and guardrails, and observability and deployment.
| Languages & Application Engineering | PythonTypeScript/JavaScriptJavaC#GoSQLthe client's application framework |
| Models & Provider Platforms | OpenAIAnthropicGoogle Gemini/Vertex AIAzure AIAmazon Bedrockapproved open-weight models |
| Agent SDKs & Orchestration | OpenAI Agents SDKLangGraph/LangChainGoogle ADKSemantic KernelAutoGenCrewAIprovider SDKs |
| Tools, MCP & Integrations | Structured function callsModel Context ProtocolREST/GraphQLwebhooksqueuesCRMERPITSMcustom connectors |
| Retrieval & Knowledge | Embeddingshybrid searchrerankingPineconeWeaviatepgvectorElasticsearch/OpenSearchgoverned document pipelines |
| Memory, State & Workflow | PostgreSQLRedisdurable workflow/state storesTemporal and explicit sessioncheckpointapproval state |
| Evaluation & Testing | Golden datasetstask-level gradersmodel/tool mocksadversarial casesregression suitesprovider evaluation tools |
| Safety, Security & Governance | Least privilegesecretssandboxingallowlistsguardrailsPII controlsapprovalsaudit logsred teaming |
| Observability & Operations | Tracesstructured logsOpenTelemetryLangSmith or equivalentlatency/token/cost dashboardsalertsrollback |
| Cloud, Containers & Delivery | AWSAzureGoogle CloudDockerKubernetesserverless runtimesCI/CDinfrastructure as code |
| User Experience & Channels | Web/mobilechatemailSlack/TeamsWhatsAppvoice/realtimereview queuesaccessible interfaces |
Hire dedicated AI developers for your project with a quick, efficient hiring process. Build your AI engineering capacity faster with matched, evaluated talent.
Tell us the agent workflow to build, the tools and data it needs, and the deployment environment so we can match developers precisely.
Review matched AI agent developer profiles and interview candidates on architecture, evaluation, tool integration and security.
Confirm scope, access and onboarding readiness—including contracting, data/system access, security review and owner availability before committing to a start date.
Hire AI agent developers who deliver secure, well-evaluated agent capabilities across a wide range of industries.
Manufacturing
Logistics
Retail
eLearning
Fintech
Agriculture
Travel
Casino
Sports
Healthcare
Real Estate
Facility
Production agents cross software, data, cloud, security, UX and operations. DreamzTech can match the core developer and connect adjacent specialists — including our MLOps engineers, NLP developers, n8n developers and Claude Code developers — when the workflow crosses role boundaries.
Share the workflow, systems, data, action boundaries and production goals. We will respond with the likely developer profile, readiness questions and a practical first scope.
Got questions about hiring AI agent developers? Explore the FAQs below to learn how DreamzTech matches AI agent developers to your workflow, tools and risk requirements.
An AI agent developer designs software that can interpret a goal, choose a permitted next step, use tools or data, maintain relevant state and return or execute a result. Production responsibilities usually include workflow design, model and tool selection, RAG or memory, integrations, evaluations, guardrails, observability, deployment, cost control and handoff—not only prompt writing.
A chatbot mainly exchanges messages and may answer from instructions or knowledge. An AI agent can also plan or route work, call tools, update systems and continue across several steps toward an outcome. Some chatbots include agent capabilities, so classify the product by what it can access and do, not by the interface alone.
An AI agent is a software component that perceives context, makes bounded decisions and takes actions. Agentic AI describes the wider design approach or behavior, which may involve one agent, several agents or a workflow combining models and deterministic code. In hiring, the terms often refer to the same engineering skills; define the workflow and controls.
Yes, when the systems expose suitable APIs, events, databases or approved connectors and permissions can be controlled. Map every read and write action, identity, data classification, error path and owner. Preserve existing authentication and audit controls instead of giving the agent unrestricted access.
Build an evaluation set from real tasks, edge cases and known failures; test completion, grounding, tool arguments, permissions, harmful inputs, escalation, latency and cost. Apply least privilege, secrets isolation, allowlisted tools, validation, human approval for consequential actions, audit logs, rate limits, monitoring and rollback. Re-run tests when models, prompts, tools or data change.
A bounded proof of concept may take a few weeks, while a production agent usually takes longer because integrations, data access, evaluations, security, user experience, deployment and owner sign-off must be completed. Estimate after reviewing one workflow, existing systems, representative examples, action risk and acceptance criteria; a demo timeline is not a production commitment.
Cost depends on seniority, workflow complexity, integrations, data preparation, evaluation depth, security, deployment, support and working-hour overlap. DreamzTech may publish $20 per hour or $3,200 for a 160-hour monthly allocation only after sales confirms applicability. Model/API usage, cloud, search/vector services, licenses and extended support are separate unless included by contract.