I build autonomous AI agents that browse the web, read documents, call APIs, and make multi-step decisions — without human input. Built with OpenAI Assistants API, LangChain, and Agno for real business workflows.
These are not chatbots. These agents plan a sequence of steps, execute them autonomously, and report results — just like a human junior analyst would, but at machine speed.
Autonomously searches public filings, scores securities disclosure risks with a custom scoring system, and populates a structured database — replacing hours of attorney research per week.
Reads incoming emails, classifies intent (interested / objection / unsubscribe / meeting request), drafts the appropriate reply using company voice guidelines, and sends or queues for human approval.
Extracts structured data from PDFs, invoices, contracts, and scanned images — validates against business rules, flags anomalies, and writes clean records to your database or CRM.
Web-browsing agent that monitors competitor websites, news sources, and social media — summarizes findings, scores relevance, and delivers a structured briefing to your Slack or email on schedule.
Scores incoming leads against your ICP criteria, enriches with LinkedIn and company data, drafts a personalized intro, and routes qualified leads to the right sales rep automatically.
Powers the Lavanhu AI system — autonomously generates teacher activity reports, incident summaries, and administrator briefings based on logged school data. Deployed with Django backend.
Best for: stateful agents with persistent threads, file search over documents (RAG), code interpreter for data analysis, and function calling to external APIs. I use this for production deployments requiring reliability and low latency.
Best for: complex multi-agent pipelines, custom tool definitions, memory management, and RAG (Retrieval Augmented Generation) systems over large document corpora. I use LangChain when the agent needs to reason across many tools or data sources.
Best for: lightweight, fast agents that need to be embedded in existing Python applications. Agno's minimal overhead makes it ideal for high-throughput business automation where adding the full LangChain stack would be overkill.
An AI agent is a software system powered by an LLM (like GPT-4) that takes autonomous multi-step actions — browsing the web, calling APIs, reading documents, writing to databases, and making decisions. Unlike a chatbot that answers a single question, an agent plans and executes a sequence of actions to complete a goal.
I implement guardrails at multiple levels: structured output schemas (so the agent always returns data in a defined format), tool-level validation (checking outputs before they're written to databases or sent as emails), human-in-the-loop approval steps for high-stakes actions, and comprehensive logging so you can audit every decision the agent makes.
Yes. I deploy agents as FastAPI endpoints that n8n or Make.com call via HTTP request nodes. This means you get the full power of custom AI agents combined with the visual workflow building and broad integrations of n8n/Make.com — the best of both worlds.
A simple single-tool agent (e.g., document Q&A) costs $500–$1,200. A multi-tool agent with web browsing, API calls, and CRM integration runs $1,500–$4,000. Multi-agent systems with orchestration are scoped individually. All projects are fixed-price.
Yes — though I prefer to call them AI agents because they do more than chat. I build customer-facing agents that handle support queries, qualify leads, book meetings, and retrieve account information — all integrated with your CRM and deployed via your website, WhatsApp, or Slack.
Book a free call to scope your agent — what it needs to do, what tools it needs, and how it fits into your existing workflow.