Case Study · agenticai01.tech

agenticai01.tech — Claude API Tool Use in Production

94%

Approval Rate

Rogue Actions

Models Integrated

Agent Tasks Automated

!The Challenge

AI agent demos online are mostly chatbots — simple Q&A with no real tool use. The challenge was building a public showcase of what real autonomous agents look like: tool-use loops, multi-model routing, approval workflows, and persistent memory.

⚙The Strategy

Full agent infrastructure on Next.js + Supabase. Claude API with tool-use loops for research, drafting, and action proposals. Groq (Llama 3.3 70B) for fast inference. DeepSeek R1 for reasoning. Supabase agent_memory for persistent context. Approval workflow UI — agents never act without human review.

✓The Results

12 agent task types automated. Multi-model routing across 3 providers. 94% agent-proposed actions approved on first review. Full audit trail in Supabase. Zero rogue actions since launch.

The Challenge

The AI agent space is full of demos that are actually just prompt wrappers. Real agents need: tool use (reading databases, calling APIs, writing to queues), memory (context that persists across runs), multi-model routing (right model for right task), and guardrails (never act without human approval).

The goal was to build and publicly demonstrate exactly this — and prove the architecture works in production.

The Architecture

Model Layer

—Claude 3.5 Sonnet — primary reasoning, tool orchestration, complex tasks
—Groq Llama 3.3 70B — high-frequency, low-latency tasks (summaries, classifications)
—DeepSeek R1 — deep reasoning tasks (strategy analysis, multi-step planning)

Routing logic: task complexity score determines model. Simple → Groq. Complex → Claude. Reasoning-heavy → DeepSeek.

Memory Layer (Supabase)

—agent_memory: persistent business context per agent
—business_context: 7-section knowledge base injected into every run
—agent_tasks: full run logs with token usage
—pending_actions: proposed actions awaiting human approval

Guardrail Layer

Agents NEVER write directly to business tables. Every proposed action goes to pending_actions with status: ''pending''. Admin UI shows the proposal with full context. Human approves → real INSERT executes. Rejects → logged with note. No side effects on rejection.

Tool Definitions (12 tools)

—Read tools: blog posts, clients, newsletter stats, business context
—Write tools: create_blog_post, create_client_task (both route via pending_actions)
—Memory tools: read_agent_memory, write_agent_memory
—Analysis tools: content performance analysis, topic suggestions

Results

The approval workflow proved to be exactly the right pattern. 94% of agent-proposed actions were approved on first review — the business context injection gave agents enough situational awareness to be genuinely useful. The 6% rejected were cases where agents misread a nuanced client context.

Zero rogue actions. Full audit trail. The architecture is production-ready and can extend to any business workflow.