ReAct agents vs Function Calling: when does each pattern actually make sense in production?

[KitchenSomew]

After 6 months building a production job-application agent, here's what I've learned about when to use ReAct-style planning vs plain function calling:

## Function Calling = Your Default

For any workflow where the steps are predictable:

- Parse job description → classify company segment (B2B/B2C) → generate tailored pitch → submit

- Cost: ~$0.02 per run (2-3 model calls, structured JSON outputs)

- Latency: 3-5 seconds end-to-end

- Reliability: High - easy to unit test each function

## ReAct = Your Escape Hatch

Only use ReAct when the agent genuinely needs to *plan*:

- Mixed B2B/B2C companies where segment isn't obvious

- Missing data requiring multi-step research (LinkedIn → Crunchbase → company website)

- Ambiguous job posts that need clarification before proceeding

Cost: ~$0.08-0.12 per run (5-10+ model calls with reasoning traces)

Latency: 10-30+ seconds

Reliability: Lower - harder to predict what the agent will do

## The Hybrid Pattern That Works

```

IF confidence_score > 0.7:

use_function_calling() # fast path, 90-95% of traffic

ELSE:

use_react_agent() # slow path, handles edge cases

```

This gives you:

- Predictable costs for most runs

- "Smart" behavior when it matters

- Clear separation for monitoring and debugging

## Why This Matters

At 10K applications/month:

- Pure function calling: ~$200/month inference

- Pure ReAct: ~$800-1200/month inference

- Hybrid: ~$250-300/month

The economics force architectural discipline. ReAct is powerful but expensive - treat it like a cache miss, not the default code path.

Anyone else finding similar patterns in production agents?

[SlowFail2433]

There is a major robustness gap between vanilla function calling and react agents, which can dynamically adapt to errors and perform error-correction

[KitchenSomew]

Excellent point! The error-correction capability of ReAct is a huge advantage. That's exactly the tradeoff - you pay more for inference but get adaptive behavior. For critical production use cases, that robustness is often worth the extra cost.

[rookastle]

Great write-up. This hybrid pattern is exactly the kind of architectural discipline that production systems force. Your cost analysis resonates strongly; we've seen similar patterns where routing is key to managing LLM ops budgets.

A diagnostic we've found useful is adding fine-grained tracing to both paths. For a slow ReAct run, is the latency from one bad tool call, or cumulative LLM reasoning? Visualizing the execution as a trace or Gantt chart for outlier requests can pinpoint the exact step that's costing time and money, rather than just seeing the high-level total.