Autonomous and multi-step AI Agents
We develop AI agents capable of breaking down complex objectives into steps, using external tools (APIs, databases, browsers), remembering context, and collaborating with other agents. A new category of automation that operates where traditional workflows cannot reach.
Autonomous agents that reason, plan, and act to complete complex tasks in multiple steps.
Use cases
- Automated competitive research
- Proactive customer success (agents that monitor and act)
- Multi-source financial analysis
- Automated procurement
- Internal personal assistant for executives
Measurable benefits
- Automation of tasks requiring reasoning
- 24/7 operations on complex processes
- Reduction of handoffs between systems and people
- Continuous learning from feedback
Technical details
Framework
- LangChain, LangGraph, LlamaIndex
- CrewAI for multi-agent
- Autogen for inter-agent dialogues
- Custom orchestration on Python
Reasoning
- ReAct, Chain-of-Thought, Tree-of-Thought
- Planning with LLM-based planner
- Self-reflection and self-correction
- Guardrail and validation steps
Memory
- Short-term: conversation buffer
- Long-term: vector DB (Pinecone, Qdrant)
- Structured episodic memory
- Dynamic knowledge graph
Tool integration
- REST and GraphQL APIs
- SQL/NoSQL databases
- Browser automation (Playwright)
- Code execution sandbox
FAQ
What is the difference between a chatbot and an AI agent?
A chatbot responds. An agent plans, executes actions in the world (APIs, DBs, browsers), and verifies the result. It is proactive, not reactive.
How reliable are agents?
With guardrails, validation, and human-in-the-loop on critical steps, they reach production-ready levels. Without them, they remain experimental.
Do they integrate with my systems?
Yes, via tool/function calling: every API or database in your company becomes a 'tool' that the agent can invoke.