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Agents often fail because they are missing the domain knowledge needed to make the right judgment. More model capacity helps, but it does not guarantee that the agent has the specific operating rule, company policy, or expert heuristic that the task requires. This knowledge usually appears during review. A reviewer notices the agent made the wrong call, explains the correction, and then needs that correction to matter the next time a similar situation appears.

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A financial-analysis example

Imagine an agent that answers questions about company liquidity. It can retrieve the current assets, current liabilities, and quick ratio. It can even calculate the ratio correctly. The failure is in the judgment layer. A quick ratio below 1.0 usually deserves caution, but the agent may still call the company liquid because management used positive language elsewhere in the filing. The lesson is a domain rule:
When assessing liquidity from a quick ratio below 1.0, do not call the company healthy without checking whether other evidence offsets the short-term liability risk.
The agent could rediscover that rule by reading many filings, comparing ratios, and reasoning through the domain from scratch. That is slow. It is also not reliable enough when the same class of mistake can reappear in production.

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Why normal context is not enough

Putting all known guidance at the top of every prompt does not scale. The agent will ignore some of it, the context window gets crowded, and irrelevant rules can distract from the rules that matter now. Leaving the guidance out is also risky. If the agent does not receive the liquidity lesson when the next liquidity question appears, it can repeat the same mistake. SovaraDB is built for this gap. It stores durable domain lessons and makes them available as targeted runtime context. Sovara run view showing the trace that revealed missing domain knowledge Sovara run view showing the trace that revealed missing domain knowledge Next, learn what Sovara stores in SovaraDB: priors.