Agent keeps changing after new integrations
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We've gradually connected our AI agent to more internal systems, so it now works with documents, tickets, CRM records, and several external APIs. The strange part is that every new integration seems to affect the agent's behavior in places that have nothing to do with the new feature. A workflow that worked perfectly a month ago suddenly becomes less reliable after adding another tool, even though we never touched its prompts. How are teams optimizing AI agents as they grow without having every new integration introduce unexpected regressions?
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Every new tool adds another description and another possible action to the agent’s context. Even when the main prompt stays untouched, those additions can change which tool the model picks and how it interprets an older request. Treat tool definitions like versioned prompts and keep their jobs as narrow as possible. Before releasing an integration, replay the workflows that already matter and compare both the final answers and the tool calls with the previous version. That will show whether the regression came from the new tool itself or from the agent choosing differently because the available options changed.
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Regression checks can reveal that an older workflow broke, but they won’t automatically find a better configuration for every type of request. Once an agent has many tools, one fixed combination of model, temperature, prompt style, and permissions rarely works equally well everywhere. You can do agent optimization here: https://eignex.com/ . It lets you define which combinations are allowed and learn from the score of each run without crossing those limits. The agent can adjust how it handles different requests while the rules around cost, models, and tool access remain in place.
