Build your AI Agent in 15 mins
Enterprise-grade agentic AI platform for building and running agents
- no coding required.
From startups to Fortune 500s, the world’s leading teams rely on DronaHQ.
Everything your agent needs, in one unified platform
DronaHQ combines modular configuration with production-grade orchestration, so you can go from idea to live agent without writing brittle scripts or prompt chains.
Securely connect AI agent tools
Bring enterprise apps, services, MCP servers, and vector stores as agent-ready endpoints using a library of over 5000 readily available AI tools.
Natural language instructions
Add LLM model of your choice
Knowledge base
Test and evaluate
Enteprise AI agent use cases
Build for real-world scenarios where AI agents can transform operations and accelerate your team’s productivity.
Protect your data and systems at every step
DronaHQ ensures your AI agents operate securely with scoped access, robust authentication, strict role controls, and full auditability, all designed to meet enterprise compliance and governance standards.
Your dedicated forward deployed engineer
DronaHQ provides dedicated forward deployed engineers who collaborate with your team to orchestrate AI agents, integrate systems, and accelerate deployment for faster business impact.
Start building powerful agents
Deliver AI agents that transform workflows and unlock new levels of operational efficiency.
Frequently Asked Questions
An AI agent is a task-specific, goal-driven worker powered by LLMs. It can reason through inputs, call tools, reference your internal knowledge, and complete actions – all within a structured and secure framework.
MCP stands for Model Context Protocol. It allows agents to interface with internal systems via tools that have clearly defined inputs, outputs, and scopes. MCP tools enable secure, structured, and observable interactions.
Yes, you can choose from supported providers like OpenAI, Gemini, Claude, or bring your own. You can also configure model behavior per agent.
Orchestration refers to how various components of the agent – models, tools, knowledge, and instructions – work together in a loop. It’s the runtime logic that enables agents to think, act, and iterate.
Single-turn agents complete a task in one go (e.g., summarize this PDF). Multi-turn agents go through multiple steps or decisions before completing a task (e.g., extract insights, then call APIs, then generate a report).
Rules define task-level behavior (what the agent should/should not do), while guardrails are system-level enforcement for safety, such as output moderation, access restrictions, or timeout limits.
Agents are autonomous and iterative – they can decide, retry, and reason across multiple steps. Unlike single-shot prompts or linear workflows, agents operate in a loop and can chain tools and knowledge until a goal is reached.
A knowledge base provides reference context (documents, SOPs, FAQs) for better language understanding. Tools, on the other hand, allow agents to perform actions, like querying a database or calling an API.
Agents can be invoked via chat, email, API, scheduled workflows, or webhook-based triggers. You can also define routing rules based on incoming payloads.
No coding is required to get started. The builder includes templates and an intuitive point-and-click interface, but if you’re technical, you can go deeper with custom logic, API calls, and model configurations.
