25+ Claude Code and Codex Unattended Agent Loops: Practical Batch Research with Terra

One-sentence takeaway

Running an automated site that researches and curates events across 22 cities daily, the author found that Sonnet 5, GPT-5.6 Terra, and even Haiku reliably completed the same task suite with far less quota pressure than Opus; however, this report is not a controlled multi-model benchmark.

Test environment

• Project: aievents.now; one agent per city, researching and curating upcoming events every morning.

• Scale: Approximately 25 agents running continuously over a month across 22 cities; roughly 30 minutes per city run; automated via cronloop running Claude Code and Codex.

• Comparisons: The author switched and tested city agents from Opus to Sonnet 5, GPT-5.6 Terra, and Haiku, aiming to identify the most cost-effective model that remains sufficiently reliable.

• Evaluation criteria: No formal scoring metrics; the author focused on hallucination rates, missed events, runtime duration, 5-hour rate-limit pressure, and long-term log behavior.

Input and configuration

The original post did not disclose full prompts, model snapshots, temperature settings, tool permissions, or per-run raw outputs. Instead, it shared a reusable agent operational workflow:

1. Set strict time limits per agent run to prevent context and instruction bloat from causing workflow explosion.

2. Stagger city runs by delaying each subsequent city start time by 15 minutes, maintaining a concurrency of approximately 3.

3. Run cost/quality pilots with smaller models first, selecting the lowest-cost model that reliably completes the task.

4. Persist complete run logs and prompt agents to analyze their past N runs to identify inefficiencies and refine operating instructions.

5. Write operational learnings into persistent Markdown files at the end of each run, which are read at the start of the next run. The author reported that after several weeks, this drastically reduced redundant research and recurring errors.

Key results

• The author reported that Sonnet 5, GPT-5.6 Terra, and Haiku were "rock solid" for this event-research task, delivering quality broadly comparable to Opus while significantly lowering usage quotas. The original post did not provide per-model numerical success rates.

• A runtime of ~30 minutes per city, a 15-minute stagger interval, and a concurrency of ~3 reflect the author's real-world operational setup, serving as a baseline for reproduction experiments.

• Persistent memory helped agents log dead sources, low-quality venues, and reusable JSON endpoints. This represents a workflow-level gain rather than an isolated measurement of Terra's standalone model capability.

Conclusions

Real-world evidence suggests that Terra is well-suited as a mid-tier execution model for long-running batch research and curation agents—especially when tasks can be time-boxed, staggered, logged, and automatically audited. It should not be reflexively promoted to a top-tier planner where maximum reasoning quality is critical; teams should first benchmark Luna, Terra, Sol, and other models against their own task logs.

Limitations

• Single-project, single-author scope with no blind evaluation, no fixed task benchmark suite, and no per-item metrics; it represents an observational field report.

• The assessment of being "broadly on par with Opus" is an overarching subjective observation that cannot predict performance on more complex tasks, other languages, or shifting website structures.

• Factors such as cronloop, subscription-tier quotas, and agent tooling harnesses heavily influence practical costs and stability, which do not translate 1:1 to pure API pricing.

Reproduction steps

1. Select 20–30 research tasks across identical cities or topics, fixing the source list, tooling environment, and execution time limits.

2. Run at least 10 trials each using Terra, Luna, Sol, and a baseline model, staggering starts to control concurrency.

3. Record all inputs, tool calls, outputs, elapsed runtimes, token counts, costs, hallucinations, omissions, and required human edits for each run.

4. Set up an A/B comparison between persistent Markdown memory and a memory-less baseline, reporting standalone model performance and memory workflow enhancements separately.