AquaMind AI
An autonomous AI agent for reverse osmosis plant operations that investigates anomalies, retrieves engineering knowledge, evaluates interventions, and supports controlled maintenance decisions.
572
Tests Passed
100%
Test Coverage
7
Tool Calls / Trace
01
The Problem
Reverse osmosis systems generate operational data continuously, but detecting degradation, investigating its cause, and selecting an appropriate response often requires manual engineering analysis across multiple sources and tools.
02
The Solution
AquaMind AI explores how an autonomous but controlled AI agent can investigate RO operational scenarios end to end. The system combines LLM reasoning with engineering tools, verified knowledge retrieval, persistent memory, permission controls, cost tracking, and auditability.
03
My Role
Creator and sole developer. I designed the agent architecture, implementation, MCP integration, memory layer, permission system, safety controls, evaluation traces, automated tests, deployment, and technical documentation.
04
Architecture
Operational data or investigation request
Plan–Act–Reflect agent loop
Approved engineering tools
MCP knowledge retrieval
Persistent SQLite memory
Permission and human-approval layer
Cost tracking and audit logging
Final investigation result or controlled action
05
Key Highlights
06
Results
572 automated tests passed
100% test coverage in final verification
Verified seven-turn / seven-tool-call agent execution
Persistent memory verified across runtime restart
MCP client/server integration verified
Task-level token and cost tracking validated
07
Challenges
Balancing autonomous reasoning with deterministic operational safety controls
Maintaining reproducible traces while using a live language model
Separating genuine task failure from successful safety enforcement
Preserving state across runtime restarts
08
What I Learned
Agent reliability requires more than strong prompting; permissions, termination conditions, auditability, evaluation, and deterministic safeguards matter.
Human approval should be part of the architecture for consequential actions rather than added as an afterthought.
Evaluation traces are essential for proving agent behavior instead of merely describing it.
Technology