AI Agents & Workflow Automation

AI Agents & Workflow Automation — LLMs That Take Real Actions, Not Just Chat🔥

The problem💯

Most "AI agents" on the market are still chatbots wearing a costume. They can answer questions, summarize docs, and sound convincing but they can't actually do anything inside a real system. They don't update records. They don't call APIs. They don't make decisions that matter.

When teams try to put them into production workflows, they either hallucinate, break under edge cases, or require so much guard railing that the agent becomes slower than the manual process it was supposed to replace.

What I built🔥

- Production AI agents that operate inside real workflows, taking structured actions, calling internal tools and APIs, working through multi-step decisions, and knowing when to ask a human.

- The agents handle the operational work teams actually want automated: querying data and answering follow-ups, classifying and routing incoming items, updating records, generating reports on request, running multi-step workflows that combine several tools, and recovering gracefully when something doesn't fit the expected pattern.

Outcomes🙌

- Agents that take real actions, not just generate text

- Tool using LLMs with structured outputs and validation layers

- Multi-step workflows that complete reliably end-to-end

- Audit logs so every agent action is reviewable

- Human escalation built in agents know what they shouldn't decide

- Evaluation suites so improvements can be measured, not guessed at

Stack🧠

Python, LLM orchestration tooling, async pipelines, structured outputs, evaluation frameworks.

Best fit for👍

SaaS products adding in-app AI assistants, ops teams automating internal workflows, fintech and back-office products with repetitive decision work, and any team that's tried building agents and watched them fall apart in production.