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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

How we design an agent that operates across systems

  1. Process decomposition — We break the process into observable steps: what information is required, in what order, what marks a step as complete.
  2. Tool definition — Every agent action goes through a declared tool with typed input and output — no free-form access to business systems.
  3. Permissions — The agent receives least-privilege, environment-specific credentials, read-only wherever writing is not required.
  4. State management — Case state is persisted, so an interruption never produces duplicates or lost steps.
  5. Output validation — Before any externally visible action, output is validated against a schema and explicit business rules.
  6. Human approval — Actions with financial, contractual or reputational impact require confirmation from an identifiable person.
  7. Logging and audit — We record prompts, invoked tools, parameters and outcomes: without traceability an agent cannot be verified or corrected.

We deliver tool definitions, the permission matrix, approval rules and structured execution logs.

Working with us

The team that analyses the process is the team that builds and maintains it: product, engineering, integration with your existing systems, governance of automated decisions and post-release support.

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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.