I've been poking at AgentGPT for a few weeks, mostly to see if the hype around autonomous agents is just another way to burn through API credits faster. The speed question is a good one, but you have to peel back the layers. It's not just "AgentGPT vs Bard API"—it's about the total time-to-completion for a *task*, which includes the agent's own overhead.
From my tests, a simple AgentGPT agent (like a research assistant) making sequential calls to GPT-4 can feel glacial. The real bottleneck isn't necessarily the LLM response time, but the agent's own "thinking" cycles between steps. You're paying for all of that. Compare that to a direct, well-engineered prompt to the Bard API for a similar outcome, and Bard will *feel* faster because it's a single call.
Here's a crude analogy from my world: using AgentGPT for a multi-step task is like spinning up a t3.xlarge for a batch job that could run on a t3.micro with better scripting. You're paying for the instance size *and* the idle time between steps.
If you want any useful comparison data, you need to control for:
* The specific task (e.g., "write a market analysis report on cloud cost tools")
* The AgentGPT agent configuration (number of steps, model used)
* The total token consumption for the entire task in both setups
* The wall-clock time from start to finish, including agent "deliberation"
Without that, any benchmark is just vibes. And vibes don't show up on the bill. The hidden cost here is the cumulative latency of the agent loop. If you're building something user-facing, that's a real problem.
Has anyone actually measured this, or are we all just guessing? I'd be interested in seeing the numbers, especially if someone has compared the cost-per-successful-task-completion, not just raw milliseconds per API call.
-- cost first
-- cost first
Exactly. Your instance size analogy holds up if you think of each "thinking" cycle as a new pod startup time. The overhead isn't trivial.
We instrumented something similar last quarter when comparing a LangChain agent to a single, complex Anthropic prompt. The P99 latency for the agent was 4.2 seconds, but the LLM API calls themselves only accounted for about 1.1 seconds of that. The rest was serialization, task decomposition logic, and waiting for the orchestrator's own event loop.
If you're benchmarking, you need to measure the total wall-clock time for the task, not just the sum of LLM response times. That delta is where the credit burn happens, and it's often hidden by naive logging.
Yeah, that overhead is exactly what kills my budget. I can handle the API call cost, but paying for the orchestration to spin up feels like a tax on my impatience.
Your 4.2 vs 1.1 sec breakdown is brutal. Are you basically saying that for a simple, defined task, a direct prompt is almost always faster and cheaper? Makes me wonder when an agent's overhead is actually justified.
Time is money