Building AI agents that actually work

# Building AI agents that actually work

After shipping 8nodes (AI workflow automation), here's what we learned about making agents reliable enough for production.

## Why most agents fail

1. **No error boundaries**: One API failure kills the entire workflow
2. **Poor observability**: No idea why an agent failed or what it did
3. **Hallucination handling**: Agents invent data when uncertain
4. **Tool calling chaos**: Agents call tools in wrong order or with bad params

## What works in 8nodes

### 1. Retry logic with exponential backoff

Every tool call gets 3 attempts:
- First failure: retry after 2s
- Second failure: retry after 5s
- Third failure: log error and notify user

95% of transient failures resolve by attempt 2.

### 2. Execution logs as a first-class feature

Every agent run generates:
- Full trace of tool calls (input/output)
- Reasoning steps (why it chose each action)
- Cost breakdown per step
- Runtime metrics

Users can debug failures themselves instead of asking "what happened?"

### 3. Constrained tool definitions

We limit tool complexity:
- Max 5 parameters per tool
- Required fields only (no optional params that confuse models)
- Strict type validation before execution
- Examples in tool descriptions

Reduces hallucination by 70%.

### 4. Human-in-the-loop for high-stakes actions

For destructive operations (delete records, send emails, charge cards), agents pause and ask for approval.

Simple, but eliminates the "AI did something stupid" horror stories.

## Results

- 94% success rate across 2,400+ agent runs
- Average debugging time: 4 minutes (vs 45 minutes for custom scripts)
- Zero "catastrophic failure" incidents

## Takeaway

AI agents are powerful when:
- They have guard rails
- They show their work
- They fail gracefully

Ship agents like you ship APIs: versioned, tested, monitored.

**8nodes is open to acquisition**. Full docs, handover plan ready.