Agents face the same operational debt as microservices—prepare now

Navan's architects argue agentic AI is following the same adoption curve as microservices circa 2015: early excitement, emerging patterns, but immature operations. The runtime layer is largely solved with cloud providers offering stable execution environments, but critical gaps remain in observability, testing, and cost governance. Teams struggle with non-deterministic agent behavior and

lack reliable debugging methods. The biggest operational challenge is cost unpredictability. Agents consume tokens in opaque ways, and vendors benefit from higher token usage. Debugging agent failures requires new approaches—traditional logs are insufficient because agents produce too much reasoning output. Navan uses interception hooks and auto-traces to capture decision points and

confidence scores. Testing remains fundamentally unsolved because agents are non-deterministic. Navan uses trajectory evaluation to measure how far an agent deviates from an expected path, but this is still immature. Their pragmatic advice: master single-agent loops before attempting multi-agent orchestration, similar to the 'well-structured monolith first' wisdom from the microservices era.

Governance and authorization are increasingly complex as agents act on behalf of users with blurred accountability. The industry is converging on MCP for tool calling and A2A for inter-agent communication, but these standards are still evolving. The bottom line: teams should invest in observability and cost management before scaling agent deployments.