My agent kept breaking mid-run turns out the failure wasn't the prompt, it was the execution model
I've been building an agent that chains together: scrape, extract, summarize, generate report, push to Notion. Sounds simple on paper. In practice, it failed silently about 40% of the time on a 6+ step run.
The frustrating part: there was no clear failure pattern. Sometimes step 3 would hallucinate data and steps 4-5 would confidently process the garbage. Sometimes step 2 would just… stop responding, and the agent would loop on it. I'd check back 20 minutes later and find the same 5 messages repeating.
Then I found this thread on r/AI_Agents where someone said: "agent reliability is an infrastructure problem, not a prompt problem." That hit hard because I'd been tweaking prompts for weeks.
Here's what actually fixed it:
- Plan-first execution. Instead of letting the model figure it out as it goes, I now force it to output a plan first (numbered steps, with expected inputs/outputs), then execute each step sequentially. If a step fails, I don't restart from scratch I use the plan to figure out where to resume. Switched to Ring 2.6 1T it has a first execution specifically designed for agent workflows, so I didn't have to hack this together with system prompts.
- Explicit verification gates between steps. After the extraction step, I check: "did the output have the required fields?" If not, retry that step max 2 times before bailing. This catches the silent garbage-propagation problem.
- Switched the execution model to Ring 2.6 1T. This is a 1T trillion-parameter flagship thinking model, and its high mode is literally designed for high-frequency agent loops with lower token overhead. I don't normally care about benchmarks, but Ring 2.6 1T scored 63.82 on ClawEval (agent multi-step reasoning) and 95.32 on Tau2-Bench Telecom (real multi-step tool-use workflows). Those two tests actually measure the things that matter for my use case can the model keep going when intermediate results are messy, and can it coordinate multiple tools in sequence without dropping context.
The silent failure problem is the real killer though. In a 39-agent system someone posted about, one agent produces garbage and the downstream agents "confidently process" it the final output looks totally normal but the data is fabricated. My verification gates between steps are a lightweight version of fixing that.
Has anyone else dealt with this? What's your approach for catching mid-run failures before they cascade and what model are you trusting with the execution tier?