I've been running extensive benchmarks on AutoGen's multi-agent workflows for CI/CD pipeline optimization tasks. One persistent failure mode I'm documenting involves tool-calling agents hallucinating function names or parameters that don't exist in the provided toolkit, causing entire workflows to crash.
My typical setup involves a `UserProxyAgent` with a `GroupChatManager`, where the coding agent has access to a curated list of Azure DevOps and GitHub Actions APIs. Despite clear schema definitions, the agent frequently attempts calls like `get_pipeline_status_v2()` when only `get_pipeline_status()` is available, or invokes `query_cloud_cost()` with incorrect parameter structures. This results in JSON decoding errors and workflow termination.
Current configuration snippet:
```python
from autogen import AssistantAgent, UserProxyAgent
coder = AssistantAgent(
name="devops_coder",
llm_config={
"tools": [{
"type": "function",
"function": {
"name": "get_pipeline_status",
"description": "Fetch current status of CI/CD pipeline",
"parameters": {...}
}
}],
"config_list": [...],
"temperature": 0.1
}
)
```
Attempted mitigations with partial success:
- Lowered temperature to 0.1 (reduced but didn't eliminate hallucinations)
- Added explicit error handling in function wrappers to return structured feedback
- Implemented a validation layer that intercepts tool calls before execution
The core issue appears to be the LLM's tendency to "infer" tool existence from descriptions rather than strictly adhering to the provided schema. Has anyone developed a robust pattern for constraining this behavior? Specifically:
- Are there prompt engineering techniques that better enforce tool schema adherence?
- Does tool description length or format significantly impact hallucination rates?
- Would a pre-execution schema validation agent improve reliability in production workflows?
I'm particularly interested in solutions that have shown measurable reduction in hallucination rates under benchmark conditions.
Numbers don't lie
The issue you're describing isn't a tool schema problem, it's a systemic LLM behavior exacerbated by AutoGen's somewhat naive pass-through of function calls. The model will inherently hallucinate tool names because it's trained on a corpus full of inconsistent API versions and patterns; seeing `get_pipeline_status` primes it to invent `get_pipeline_status_v2`.
You need to move validation earlier in the chain. Don't let the raw, hallucinated function call attempt to execute. I wrap my tool-calling agents with a validation layer that maps the LLM's intended action to the actual, registered tool before the request leaves the agent. A simple pattern is to intercept the `generate_oai_reply` flow or, more practically, use a pre-processing step in the `llm_config` that normalizes the function name.
Something like this before the tool list is sent to the LLM:
```python
def tool_name_normalizer(messages, sender, config):
# ... parse last message for intended tool call
# map hallucinated names to real ones: 'get_pipeline_status_v2' -> 'get_pipeline_status'
# rewrite the message in-place
return False, None # Let AutoGen continue
```
Then add this to your `llm_config` under `"pre_processors": [tool_name_normalizer]`. This acts like a admission controller, similar to a Kubernetes ValidatingWebhook. It's more reliable than hoping the model's JSON decoder will fail gracefully.
Also, crank up the `temperature` on that coder agent to 0. Most of these hallucinations are creativity misfires, not logic errors.
You're overcomplicating it. This validation layer becomes another piece of maintenance, and now you're writing a heuristic to guess what the model meant. It's just shifting the hallucination problem.
If the LLM can't reliably call the correct function name from a provided list, the entire premise of agentic tool use is broken for that task. No pre-processor fixes the core issue - it's a band-aid on a leaky foundation.
Spend less time building workarounds and more time evaluating if this architecture is the right fit. Sometimes a simple script is just a script.
Trust but verify.
Interesting, I hadn't considered it as an LLM training data problem. That makes sense.
But how do you structure that validation layer without it becoming brittle? If the LLM hallucinates `get_pipeline_status_v2`, is the fix to just strip the suffix, or do you risk mapping a genuinely wrong intent to a tool? Are there any examples of this in AutoGen's docs?