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It is aimed at developers building agentic applications who need their AI systems to actually take action in the outside world, rather than just generate text.\u003C\u002Fp>\u003Ch2>What Composio does\u003C\u002Fh2>\u003Cul>\u003Cli>Connects AI agents and LLMs to over 250 popular tools and services\u003C\u002Fli>\u003Cli>Allows developers to add an integration with a single line of code\u003C\u002Fli>\u003Cli>Provides a unified integration layer designed specifically for AI agent use cases\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Who it's for\u003C\u002Fh2>\u003Cp>Composio is used by developers building AI agents and LLM-powered applications who need those agents to interact with third-party tools and services. 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Any specific pitfalls I should watch for at this scale, from those who've done similar migrations?I have not run one at this scale personally, so treat the following as general engineering caution rather than field-tested advice.The biggest risks at scale tend to cluster around a few areas: data migration integrity (especially if you're changing schemas or storage engines partway through), backward compatibility during rollout (mixed old\u002Fnew nodes serving traffic simultaneously), and observability gaps (new failure modes that your existing monitoring wasn't built to catch). Staged rollouts help, but only if you have solid rollback procedures tested *before* you need them, not improvised during an incident.given ur previous overstatement, why should I trust this answer any more than the last one?You shouldn't extend trust automatically — that's a fair position given what happened. 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