Steer Your Agents Until They Steer Themselves

Postman describes building an enterprise agent platform to support an AI-native go-to-market organization while preserving human-led customer relationships and using AI primarily for internal analysis, research, and recommendations across large volumes of customer data. The platform addresses the limitations of individually built agents and bespoke engineering projects through a shared, configurable substrate in a monorepo, where domain teams, managers, IT, and platform engineers separately control prompts, guardrails, permissions, tools, and deployment settings. To provide fast runtime responses while benefiting from deeper analysis, Postman uses scheduled research pipelines and evolving account memories that synthesize CRM records, calls, telemetry, support data, and other sources, including Anthropic’s memory and “Dreaming” capabilities for memory maintenance. Because enterprise GTM questions are qualitative and lack objectively correct answers, the approach emphasizes decomposing performance into verifiable subproblems, such as tool-call reliability, guardrail collisions, rewrite quality, and fact overlap, rather than relying solely on human judgment or model-as-judge evaluations. The author frames agent development as a staged climb from chat tools to reusable skills, scheduled reports, proactive agents, and persistent memory, with measurable checks at each transition to prevent uncontrolled complexity. The long-term goal is not fully autonomous self-improvement but “self-healing” agents that maintain performance baselines as models, prompts, tools, and data change, enabling organizations to compare cost and quality and safely automate iterative improvements.

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