Building Production-Grade Agentic Systems with Claude

Agentic systems—AI agents that use tools autonomously to solve problems—are becoming the default architecture for production AI applications. Yet most implementations fail at scale due to poor error handling, tool selection, and reasoning loops.

Over the past 18 months, I've built several production agentic systems at scale using Claude. Here's what actually works.

What Makes an Agent "Production-Grade"?

A production-grade agent isn't just one that works—it's one that:

• Handles failures gracefully. Tools fail. Networks timeout. Agents must retry, fallback, and escalate intelligently.
• Controls execution cost. Each API call costs money. Agents must make efficient decisions about when to call tools.
• Provides observability. You need to see what the agent is thinking, what tools it called, and why it failed.
• Respects boundaries. Agents must not execute unsafe operations, loop infinitely, or exceed rate limits.

Most tutorials skip these requirements. That's why they fail in production.

The Core Pattern: Reasoning Loop with Tool Use

The fundamental pattern is simple but critical:

1. Send user message + available tools to Claude
2. Claude returns: reasoning + tool_calls
3. Execute tools, collect results
4. Send results back to Claude
5. Claude returns: final response OR more tool_calls
6. Repeat until Claude returns only text (no tool calls)

Claude's reasoning models (like Claude 3.7 with extended thinking) excel at this because they show their work. You see why the agent made each decision.

Tool Selection: The Hidden Complexity

Most teams fail at tool design, not implementation. Common mistakes:

• Too many tools. Claude will get confused. Start with 3-5 essential tools.
• Poorly documented tools. If your tool descriptions are vague, Claude will misuse them. Be explicit.
• Tools that return too much data. If a tool returns 10MB of logs, Claude wastes tokens parsing it. Return structured, compact results.
• No tool error boundaries. If a tool crashes, the agent crashes. Wrap everything in try-catch.

"The quality of an agentic system is directly proportional to the clarity of its tool definitions."

Real Example: Incident Resolution Agent

At alt.bank, we built an agent that automatically resolves production incidents:

Tools available:
1. search_logs(query, time_range) → returns structured logs
2. get_metrics(service_name, metric) → current system metrics
3. list_recent_deployments() → recent code changes
4. create_incident_ticket(title, severity) → escalate if needed

The agent receives an alert like "PostgreSQL connection pool exhausted." It then:

1. Searches logs for connection pool errors in the last 30 minutes
2. Checks current metrics (active connections, CPU, memory)
3. Reviews recent deployments to find what changed
4. Proposes a fix (restart connection pool, scale up, revert deployment)

This runs autonomously. No human needed until the incident is resolved or escalation is necessary.

Cost Control: The Overlooked Requirement

Agents can become expensive fast. Control costs with:

• Token budgets. Set max tokens per request. If Claude wants to reason more, it can't. This prevents runaway costs.
• Tool call limits. "Agent may call tools max 5 times per request." Forces decisiveness.
• Caching tool results. If the agent asked for service logs in the last call, cache them. Reuse, don't re-fetch.
• Tool response truncation. "Search returned 500 results. Show only top 10." Saves tokens.

With these controls, agents stay cheap. Without them, they become $100/request real fast.

Observability: See What Your Agent Is Thinking

Log everything:

{
  "request_id": "uuid",
  "user_query": "PostgreSQL is slow",
  "claude_reasoning": "I see high query latency. Let me check for locks.",
  "tool_calls": [
    {
      "name": "search_logs",
      "args": {"query": "lock timeout", "limit": 50}
    }
  ],
  "tool_results": [...],
  "final_response": "Found lock on users table from migration.",
  "total_tokens": 4200,
  "cost_usd": 0.18
}

This logging is critical. When the agent makes a mistake, you'll see exactly where and why. It's also the only way to find cost issues.

Boundaries: Preventing Dangerous Actions

Never let agents execute destructive operations without human approval:

• Tools that delete data should require explicit human confirmation
• Tools that deploy code should create a PR for review, not deploy directly
• Tools that access sensitive data should be rate-limited and audited

The pattern: agent recommends action → human approves → agent executes.

Key Takeaways

1. Start simple: 3-5 well-designed tools beat 50 poorly-designed ones
2. Log everything for observability and debugging
3. Control costs with token budgets and tool limits
4. Use error boundaries—tools will fail
5. Never skip human approval for destructive operations
6. Test extensively before production—agents can fail in creative ways

Agentic systems are powerful. They're also easy to build wrong. Focus on foundations first: clear tool design, observability, and cost control. Everything else follows.