AI Agent Development

A traditional chatbot only responds to prompts by generating text. An AI Agent has a reasoning loop, tools, and memory. It can break down a complex goal, select appropriate tools (e.g. look up a customer, query shipping databases), call APIs, and execute actions to achieve a goal autonomously.

We implement strict Human-in-the-Loop (HITL) gates for sensitive actions, such as writing data or making payments. We also restrict tool permissions to readonly APIs wherever possible, run agents in sandboxed environments (like secure Docker containers), and implement strict timeout and token budget safety rules.

We mainly use LangGraph and CrewAI for building multi-agent orchestrations, as they offer excellent control over states, branches, loops, and agent collaboration. We also use LangSmith for tracing, debugging, and benchmarking agent runs.

Yes. As long as there is an API, database connection, or even a command-line interface, we can build custom agent tools to interact with it. For legacy systems without clean interfaces, we can interface via RPA (Robotic Process Automation) or secure script hooks.

We set strict maximum iteration limits and budget counters on all agent loops. If an agent fails to solve a task or starts repeating actions, the reasoning loop halts, logs a trace to LangSmith, and gracefully hands over to a human operator.