AI Macro Agent
Web Access • Tool Use • Verification • Retro-Action
This page presents a basic macro-agent architecture: an AI model receives a goal, searches the web or local resources, creates a plan, executes safe actions, verifies the result, then uses retro-action feedback to improve the next cycle.
Basic Agent Loop
What the Agent Can Do
- Search the web for fresh information before making decisions.
- Read project files, logs, schemas, and API responses.
- Generate patches, code, SQL requests, and deployment steps.
- Test actions with curl, lint checks, syntax checks, and small simulations.
- Stop or revise the plan when verification fails.
Safety Boundaries
- Actions run in a sandbox or restricted workspace first.
- Dangerous commands require explicit confirmation.
- Secrets are never printed into logs or public pages.
- Database writes are backed by previews and rollback steps.
- External web data is treated as untrusted until verified.
Interactive Demo: Retro-Action Simulation
This front-end demo does not call a real backend. It shows the reasoning cycle that a production macro-agent would execute when connected to web access and controlled tools.
Technical Architecture
Frontend
Portfolio page, demo console, status display, and clear explanation of the agent loop.
Backend
FastAPI or PHP proxy that exposes safe endpoints for search, planning, execution, and verification.
Agent Core
Planner, memory/context store, tool registry, permission system, action executor, and verifier.
Retro-Action
Every action produces feedback: success, failure, confidence, logs, and next correction.
Application Message Summary
I am building web-connected AI agent systems that can plan, act, verify, and improve through feedback. My work connects web interfaces, FastAPI/PHP backends, MariaDB data, robotics UI, simulation, and system architecture. This macro-agent page demonstrates the core loop in a clean portfolio format for companies building AI model agents.