AI Agents Development

AI agents are autonomous systems that can reason, plan, and take actions to accomplish goals. Unlike chatbots that respond to messages, agents can break down complex tasks, use tools (APIs, databases, code execution), make decisions, and iterate until objectives are achieved. They operate with agency—pursuing goals rather than just answering questions.

Use agents for tasks requiring multiple steps, tool usage, or reasoning loops. Examples: research requiring multiple searches and synthesis, code generation with testing and debugging, data analysis with dynamic queries. Use simple LLM integration for single-turn tasks like classification, summarization, or straightforward Q&A.

We select frameworks based on requirements. LangChain/LangGraph for complex workflows with good observability. CrewAI for multi-agent collaboration scenarios. AutoGen for code-heavy agent tasks. Custom frameworks when we need maximum control over agent behavior. Often we combine frameworks or build custom solutions for specific needs.

We implement multiple safeguards: sandboxed execution environments, action approval workflows for high-risk operations, budget and iteration limits, comprehensive logging for audit trails, graceful degradation when agents get stuck, and human-in-the-loop checkpoints for critical decisions. Agents are designed to fail safely.

Yes. Agents are fundamentally about tool use. We create tool interfaces for your APIs, databases, internal systems, and third-party services. The agent then reasons about which tools to use and when. This means agents can work within your existing infrastructure rather than requiring a complete overhaul.