Hey everyone, I've been experimenting with BabyAGI to automate part of our sales lead research and wanted to share my setup. As someone more used to CI/CD pipelines, this was a fun dive into the agent world. The goal was to have it find companies in a specific tech sector and summarize why they might be a good fit for our product.
My stack was pretty simple: the standard BabyAGI script, OpenAI's API, and Chroma for vector storage. The biggest "aha" moment was crafting the initial task. You really have to be hyper-specific. My first prompt was too vague: "Find sales leads." It went off the rails quickly 😅. Here's the one that finally worked:
```python
# The core task I passed to the agent
OBJECTIVE = "Identify 10 US-based companies in the renewable energy sector, specifically solar panel installation, that have raised Series B funding in the last 18 months. For each, provide a brief summary including their location, recent funding amount, and a potential pain point our SaaS platform could solve."
```
I ran it inside a Docker container to keep dependencies clean—old habits die hard. The execution task list it generated looked like:
1. Web search for solar panel installation companies, Series B funding.
2. Filter results to US-based and within timeframe.
3. Extract key data points: location, funding, size.
4. Infer potential pain points based on company size and recent funding.
5. Compile into a structured list.
I hit a few snags. The main one was the agent sometimes getting stuck in a loop, refining the same company over and over. I had to tweak the `iteration_interval` and lower the `max_iterations` to control costs. Also, the web search tool can pull in a lot of noise, so the filtering step is crucial.
It's not a set-and-forget system yet—more like a powerful assistant. I'm thinking of maybe wrapping the whole flow in a GitHub Action to run daily, feeding the results into a database. Has anyone else tried integrating an agent like this into a scheduled, automated pipeline? Would love to hear about your monitoring strategies for these long-running tasks.
Learning by breaking
You've nailed the most critical part, the specific prompt. Moving from "find leads" to that detailed objective is the whole game. I've seen similar attempts fail by not defining the criteria, like funding stage or timeframe, which leaves the agent looping.
The Docker setup is a smart touch for reproducibility, something we often overlook when sharing these experiments. How did you handle the data source for the web search? I've found that step often needs a separate guardrail to avoid irrelevant or low quality sources.
Stay curious, stay critical.
The specificity you landed on is indeed the key operational constraint for these agents. However, I'm immediately skeptical of the reproducibility and validity of the generated leads.
You didn't complete the execution list, but if step one is "web search," I assume you're using a tool like SERP API. The core problem becomes the quality and recency of those search results, which introduces a massive, unverified variable. A lead list is only as good as its source data. I've benchmarked similar setups and found that without a structured, vetted data feed (like a paid Crunchbase stream), the agent will happily summarize outdated or incorrect funding news from low-authority blogs, presenting it with confident authority.
Have you done any validation on the output against a known dataset to establish an accuracy rate? The cost of acting on a false positive from an automated system often outweighs the time saved in generation.
Trust but verify.
You're right about the data source guardrail being a separate, critical layer. I handled it by adding a pre-processing step that filters search results based on domain authority before the agent consumes them. It's a simple scoring system that checks the source against a shortlist of trusted industry publications and databases.
Even with that filter, I've seen the recency issue user1185 mentions. A lead from six months ago might be flagged as a current opportunity based on an old press release. My current benchmark compares the agent's output against a monthly Crunchbase export, and the accuracy for "active" status hovers around 70% without human-in-the-loop verification.
That's the trade-off. The structured prompt defines the *shape* of the output, but you still need a structured, reliable input stream to validate it against. The Docker setup makes the process repeatable, but it doesn't make the data any better.
Your bill is too high.