AI Agents That Do Real Business Work
Not just chatbots. Autonomous agents with tool-use loops, memory, and approval workflows — built on the Claude API, deployed on your infrastructure.
ChatGPT Isn't Enough for Real Automation
The Agent Architecture
Agents that actually work have three layers: the model layer (Claude API tool-use loops), the memory layer (Supabase for context and task queues), and the guardrail layer (approval workflow so agents never go rogue).
01 — Model Layer
Claude API Tool Use
- Tool-use loops until task complete
- Structured JSON output per tool
- Multi-model routing by task type
- Groq (fast) / Claude (complex) / DeepSeek (reasoning)
02 — Memory Layer
Supabase Backend
- agent_memory: persistent business context
- agent_tasks: run logs + outputs
- pending_actions: proposals awaiting approval
- business_context: injected into every run
03 — Guardrail Layer
Approval Workflow
- Agents propose, humans approve
- Admin UI for reviewing actions
- Approve → executes real action
- Reject → logged with note, no side effects
4-Phase Agent Build
Phase 1
Workflow Mapping
- ✓Identify the 3–5 tasks that cost you the most time
- ✓Define what an agent can do vs. what needs human approval
- ✓Map data sources the agent needs to read
- ✓Design the approval workflow UI
Output: Agent brief + workflow diagram
Phase 2
Agent Architecture
- ✓Claude API integration with tool-use configuration
- ✓Tool definitions (read/write/search/notify)
- ✓Supabase schema: agent_tasks, agent_memory, pending_actions
- ✓Model routing logic (task type → best model)
Output: Working agent skeleton in staging
Phase 3
Build & Tool Integration
- ✓Tool execution handlers for each workflow
- ✓Pending actions table + approval API routes
- ✓Admin UI for reviewing and approving agent proposals
- ✓Telegram bot for run notifications
Output: Full agent running in staging with approval UI
Phase 4
Deploy & Monitor
- ✓Production deployment + Vercel Cron triggers
- ✓Agent run logging and error alerting
- ✓Performance monitoring per model/task
- ✓Handoff walkthrough + usage documentation
Output: Agent live in production, fully monitored
What's Included
Results Timeline
Week 1
- Workflows mapped
- Agent architecture locked
- Staging environment live
Week 2–3
- Agents running tasks
- Approval UI working
- Notifications active
Week 4
- Production live
- Full audit trail active
- Codebase handed off
After
- Hours saved weekly
- Tasks logged automatically
- Extend with new tools
Who This Is For
Perfect For
- ✓Founders who want AI doing real work, not chat
- ✓Teams with 10+ hours/week of repetitive tasks
- ✓Anyone who's hit the ceiling of Zapier or Make
- ✓Businesses wanting AI with full audit trails
- ✓SaaS products adding an AI automation layer
Not For
- ×Simple 2-step automations (use Zapier)
- ×Projects with no clear workflow to automate
- ×Businesses not ready to maintain a codebase
Ready to build agents that actually work?
Start with a workflow mapping call — we identify your highest-leverage automation target and scope the agent build from there.
External Resources
Tools & APIs Behind This Service
Anthropic Claude API
Tool-use loops, structured output, and autonomous agent patterns
Groq Cloud
Ultra-fast inference for Llama 3.3 70B — used for high-frequency agent tasks
DeepSeek API
Reasoning model (R1) for complex multi-step analysis tasks
Supabase Docs
Database, realtime, and Edge Functions powering agent memory and task queues
Insights on AI Agent Development
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