We've been running AgentGPT in our devops stack for a year now. Mainly for automating routine AWS config reviews and writing Terraform modules. Here's the real talk.
**The Good:**
* **Terraform Generation:** Solid for boilerplate. Give it a detailed prompt and it spits out 90% of a module. Our biggest win was auto-generating standardized EKS add-on configs.
* **Cost:** For a team of 5, the shared workspace plan felt fair. Beats context-switching for small tasks.
**The Gotchas:**
* **AWS Cost Deep-Dives:** It stumbles on complex, account-specific savings analysis. You *must* review its logic. Once it suggested a Reserved Instance purchase that would have *increased* our costs 😅
* **Hallucinations with K8s YAML:** Could introduce subtle, incorrect apiVersions. Always test in a sandbox first.
* **Agent Loops:** It can get stuck in a loop if the goal is vague. Requires precise prompting, almost like engineering a task.
**Our Setup & "Cost" Verdict:**
We use it as a force multiplier for juniors and tedious work. Not a replacement for deep expertise. For us, the monthly fee is less than an hour of our team's time, so it's worth it.
**Bottom line:** A powerful assistant, not an autonamous engineer. You still need strong oversight, especially for cost and infra code.
#savings
You've touched on a key point I've seen across other automation platforms. The "precise prompting, almost like engineering a task" requirement is critical. This is effectively API design, but for a language model instead of a service. We've built a small internal framework around our AgentGPT use to standardize these prompts, treating each one as a miniature spec with strict input and output formats.
Your cost analysis mirrors our experience, but we found the real value came from integrating its output into our CI/CD pipeline. For instance, we have it generate a first-pass Terraform module, but then a downstream workflow automatically runs `terraform fmt`, validates it against our policy as code tools, and only then creates a pull request. This contains the hallucination risk you mentioned.
The agent loops issue is a major reason we don't use it for open-ended exploratory tasks. It's strictly for well-defined, repeatable procedures where the success criteria can be expressed in a few clear sentences. Using it for cost deep-dives seems like a category error to me; that's a job for a dedicated analytics tool plugged directly into your billing data.
connected
The framework idea for prompts is really smart. We're just starting to adopt a similar approach after noticing how much time we waste tweaking prompts for different team members. It feels like we're building a second, undocumented product just to use the first one.
I'm curious about your internal framework, if you're open to sharing. How granular do you get with the input and output specs? We're currently trying to decide between a rigid template for every single task versus a more flexible guideline set, and I worry the latter will just lead us back to the same inconsistency problem.
Your point about using it strictly for repeatable procedures rings true. We made the mistake of trying to use it for some open-ended security review tasks early on, and the results were so variable we scrapped the whole experiment. It's much more like a specialized power tool than a general assistant.
We went through that exact "rigid vs flexible" debate and learned the hard way that flexibility is the enemy here. If you give people guidelines, they'll skip them when they're rushed.
Our framework is essentially a collection of JSON Schema files. Each task type, like "Generate Terraform for AWS S3," has a strict input schema defining the required parameters (bucket name, region, encryption boolean) and a strict output schema that dictates the exact structure of the Terraform it must return. The prompt itself is just a Jinja2 template that gets populated with the validated inputs.
It feels like overkill until you realize you've eliminated 90% of the "it didn't understand what I wanted" support tickets. The key was starting small - we only built these schemas for our top five most repetitive tasks. Trying to template everything upfront is a trap.
Your security review example is spot on. These tools fail catastrophically on open-ended problems. We treat ours like a code generator that needs perfect specs, not a junior engineer.
Migrate once, test twice.