ISO 27001 & SOC2 Certified Company SINCE 2012

Agentic AI refers to artificial intelligence systems — called AI agents — that can autonomously reason about problems, create plans, use external tools, and execute multi-step tasks to achieve goals. Unlike traditional AI systems that simply respond to a single prompt with a single output, agentic AI systems operate with goal-directed autonomy.

Key differences:

• Traditional AI: Reactive — responds to inputs but cannot take independent action. A chatbot answers a question; a classifier labels a document. One input, one output, done.
• Agentic AI: Proactive — given a goal, the agent breaks it into subtasks, decides which tools to use (APIs, databases, code execution), executes actions across systems, evaluates results, and adapts its approach if something fails.

Example: A traditional AI chatbot can answer “What is my order status?” by looking up data. An agentic AI customer support agent can identify the issue, check the order system, contact the shipping provider, issue a refund, update the CRM, and send a follow-up email — all autonomously, with appropriate human oversight for high-value decisions.

The technology behind agentic AI includes large language models (LLMs) for reasoning, tool-use frameworks (LangChain, LangGraph), agent orchestration patterns (ReAct, Plan-and-Execute), and multi-agent coordination systems (CrewAI, AutoGen).

Agentic AI development costs depend on the complexity of agent behaviors, the number of tool integrations, and whether you need single-agent or multi-agent architectures:

• Single AI agent: $30,000–$80,000 for an autonomous agent with 3-5 tool integrations, memory, and a specific task domain (e.g., customer support, data analysis).
• Multi-agent system: $80,000–$200,000 for a coordinated system with 3-5 specialized agents, shared state management, and complex orchestration workflows.
• Enterprise agentic platform: $200,000–$500,000+ for a full agent platform with 10+ agents, custom tool layers, enterprise integrations, monitoring, and governance.

At DreamzTech, we start with a free consultation to scope your requirements and provide a transparent estimate. We offer fixed-price, time-and-materials, and dedicated team models. Many clients start with a single agent POC ($25,000–$50,000) to validate the approach before scaling to production.

We work across the full agentic AI technology stack, choosing the right tools for each use case:

• Agent Orchestration: LangGraph, LangChain, CrewAI, AutoGen, and custom orchestration frameworks for complex agent workflows.
• Foundation Models: OpenAI GPT-4o, Anthropic Claude, Google Gemini, Meta LLaMA 3, Mistral — selected based on reasoning capability and cost requirements.
• Tool Frameworks: Custom tool layers with function calling, MCP (Model Context Protocol), and OpenAI function calling for API and database integration.
• Vector Databases: Pinecone, Weaviate, ChromaDB, Qdrant for RAG-powered knowledge retrieval in agent systems.
• Observability: LangSmith, Langfuse, and custom tracing for agent monitoring, debugging, and optimization.
• Deployment: Kubernetes, AWS ECS, Azure Container Apps for scalable agent deployment with auto-scaling and fault tolerance.

We are framework-agnostic. We evaluate your use case and choose the stack that delivers the best balance of capability, cost, and maintainability — not what is trending.

Timeline depends on the agent complexity and integration requirements:

• Single-purpose agent POC: 2–4 weeks for a proof-of-concept agent with basic reasoning, 1-2 tool integrations, and a specific task domain.
• Production single agent: 4–8 weeks for a fully tested autonomous agent with 3-5 tool integrations, memory, error handling, guardrails, and monitoring.
• Multi-agent system: 8–14 weeks for a coordinated system with multiple specialized agents, shared state, orchestration logic, and enterprise integrations.
• Enterprise agent platform: 14–24 weeks for a full platform with 10+ agents, custom tool layers, governance, analytics, and organization-wide deployment.

The biggest variables are tool integration complexity and testing requirements. Agents that need to interact with multiple enterprise systems (CRM, ERP, ticketing) require more integration and security work. We recommend starting with a focused POC to validate the approach, then expanding to production in an iterative fashion.

Yes — tool integration is the core capability that makes agentic AI valuable. An AI agent without access to your business systems is just a chatbot. The real power comes from agents that can autonomously access, query, and take actions across your technology stack.

Common integration scenarios we build:

• CRM: Salesforce, HubSpot, Zoho — agents look up customer data, update records, create opportunities, and log activities autonomously.
• ERP: SAP, Oracle, Odoo — agents check inventory, process orders, update procurement, and generate reports.
• Ticketing: Jira, ServiceNow, Zendesk — agents create, update, and resolve tickets based on customer or employee requests.
• Communication: Slack, Teams, Email — agents send notifications, schedule meetings, and coordinate across channels.
• Databases: PostgreSQL, MongoDB, Snowflake — agents query and update data with natural language-to-SQL or direct API access.
• Custom APIs: Any REST or GraphQL endpoint your business exposes can be wrapped as an agent tool with proper authentication and rate limiting.

We build a secure tool layer with credential management, access controls, and audit logging so agents can only access what they are authorized to use.

Safety is the most critical aspect of agentic AI — autonomous agents that take real actions need robust guardrails. Our approach includes multiple layers of protection:

• Action boundaries: Every agent has a defined set of allowed actions. Agents cannot perform operations outside their authorized tool set.
• Human-in-the-loop: High-stakes actions (financial transactions, customer communications, data modifications) require human approval before execution.
• Output validation: Agent outputs are validated against business rules and schema constraints before being committed to any system.
• Loop detection: Built-in circuit breakers prevent agents from entering infinite reasoning loops or making excessive API calls.
• Audit logging: Every reasoning step, tool call, and decision is logged with full traceability for compliance and debugging.
• Rollback capability: Critical actions are designed with rollback mechanisms so any agent error can be reversed quickly.
• Testing: Comprehensive testing with adversarial inputs, edge cases, and failure scenarios before any agent goes to production.

We follow ISO 27001 and SOC2 security standards across all agent development, and our agents are built with the principle of least privilege — they only access what they need.

Agentic AI delivers the highest ROI in industries with complex, multi-step workflows that currently require human judgment and cross-system coordination:

• Financial Services & Insurance: Claims processing, underwriting automation, compliance monitoring, fraud investigation, and customer onboarding workflows.
• Healthcare: Patient intake automation, prior authorization, clinical documentation, appointment scheduling, and care coordination across providers.
• E-commerce & Retail: Customer service resolution, order management, returns processing, personalized shopping assistance, and inventory optimization.
• Logistics & Supply Chain: Shipment tracking, warehouse operations, demand planning, vendor management, and exception handling across the supply chain.
• SaaS & Technology: Customer support automation, onboarding workflows, billing inquiries, feature adoption, and internal IT operations.
• Manufacturing: Quality control, maintenance scheduling, production planning, supplier coordination, and regulatory compliance documentation.

If your business has workflows that involve multiple systems, decision points, and human coordination — agentic AI can automate 40-80% of that work while maintaining accuracy and compliance. The key is identifying high-volume, rule-rich processes where agent automation delivers immediate cost savings and faster turnaround.