> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sovara-labs.com/llms.txt
> Use this file to discover all available pages before exploring further.

# The context-problem of AI agents

> Why agent context needs durable memory and retrieval.

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.

## 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.

## 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.

This is the gap Lessons handles. Sovara stores durable domain knowledge and
makes it available as targeted runtime context.

<img className="docs-screenshot docs-screenshot-light" src="https://mintcdn.com/sovaralabs/NvGxJrAQ7PUWeA2e/assets/screenshots/inspect-view-no-chat-light.png?fit=max&auto=format&n=NvGxJrAQ7PUWeA2e&q=85&s=dcfbbbca5dc2041f154e2093b36abba0" alt="Sovara run view showing the trace that revealed missing domain knowledge" width="3104" height="2144" data-path="assets/screenshots/inspect-view-no-chat-light.png" />

<img className="docs-screenshot docs-screenshot-dark" src="https://mintcdn.com/sovaralabs/NvGxJrAQ7PUWeA2e/assets/screenshots/inspect-view-no-chat-dark.png?fit=max&auto=format&n=NvGxJrAQ7PUWeA2e&q=85&s=608cdeb60418df27ff1e77ef83f75583" alt="Sovara run view showing the trace that revealed missing domain knowledge" width="3104" height="2144" data-path="assets/screenshots/inspect-view-no-chat-dark.png" />

Next, learn what Sovara stores as runtime context: [lessons](/sovaradb/lessons).
