Synthetic Data vs Real Data for Training AI Agents
Training AI agents on real interactions is getting harder. High-quality labeled data is scarce. Real-world logs are messy, fragmented, and often locked behind walls. Teams hit walls on cost, privacy, and scale. You can’t train or evaluate reliably if you don’t have enough good examples.
Real data has real limits
Think about what you need for an agent: screenshots, keyboard and mouse events, natural-language instructions, and correct outcomes. Where do you find that? The real world is noisy. Public benchmarks are tiny. Internal logs are siloed. Labeling real sessions is labor-intensive. One study of 50k browser interactions found 40 percent had no clear success label, and manual annotation cost $12 per labeled example. That doesn’t scale.
Synthetic data is a practical alternative
Synthetic data lets you generate thousands of labeled agent trajectories from scratch. You define the task, the environment, and the expected outcomes. A synthetic task might look like: open a browser, navigate to a SaaS dashboard, click through menus, fill a form, and verify success. The agent is trained to behave like a human user, but you control every step. This approach reduces labeling cost by up to 90 percent compared to manual annotation, according to internal benchmarks. You also get complete control over edge cases: error states, rare workflows, and safety-critical flows that are hard to capture in the wild.
Key tradeoffs to track
- ●Real data is grounded in lived behavior, but capturing it at scale is expensive and slow.
- ●Synthetic data can be generated quickly and uniformly, but you must validate realism.
- ●Real datasets reflect current market diversity, while synthetic data may need augmentation to match it.
- ●Privacy and compliance are simpler with synthetic data because you can scrub PII before generation.
- ●Over-reliance on synthetic data can create a feedback loop if the simulation does not map to actual user behavior.
The smartest teams combine real and synthetic data. Use real logs for validation and rare, high-stakes scenarios. Use synthetic data to build robust baselines and cover edge cases quickly.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. This lets it capture realistic interaction data, including screenshots, clicks, and text inputs. You can work with the Coasty team to produce custom synthetic datasets tailored to your agents and workflows. There’s no self-serve product and no fixed packages. The service is custom and contact-led: you talk to the team about your use case, they design a solution, and they deliver the data you need.
If you’re blocked by a lack of labeled interaction data, synthetic data can unblock you. The best next step is to talk to the Coasty data team and see how they can help you build the datasets you need. Book a data call at https://cal.com/coasty/coasty-data-call .