Nexus AI Agent
An autonomous assistant that researches, plans and gets work done.
Nexus AI Agent
An autonomous assistant that researches, plans and gets work done.
Project overview
Nexus started as a question I kept asking myself: what if a chatbot could actually finish a task instead of just talking about it? I built a prototype agent that takes a goal in plain language, breaks it into steps, uses tools like web search and summarization, and reports back with results. It is still rough around the edges — which is exactly why it is my favorite project.
Problem
Most chatbots answer questions but leave the actual work to you. Researching a topic, comparing options, drafting a summary — every step still needs a human in the loop.
Idea
Give an LLM a loop: plan the steps, call tools for each step, check the result, and keep going until the goal is done — with the human approving key decisions.
Solution
A conversational agent with a planner-executor loop. It decomposes goals into sub-tasks, calls search and text-processing tools, keeps working memory of what it has found, and produces a final structured answer with sources.
My role
Sole builder — prompt design, agent loop, tool integrations, testing and iteration.
Main features
- Goal decomposition into ordered sub-tasks
- Web search + summarization tool calls
- Working memory across multi-step runs
- Structured final reports with sources
- Human-in-the-loop approval checkpoints
Challenges
The agent was confidently wrong in early tests — looping forever or hallucinating tool results. I added step limits, result validation, and approval checkpoints, which made it slower but dramatically more trustworthy.
What I learned
Agents fail like interns, not like code. Reliability comes from constraints — clear tools, tight loops, good fallbacks — not from a smarter model alone.
Future improvements
- Persistent memory across sessions
- eval harness for regression testing
- Template library for business workflows