API Costs Are the Obvious Part
Everyone knows about the per-token or per-request costs of AI APIs. You can estimate these before you start: roughly X tokens per task, Y tasks per day, Z cost per token. But the API cost is typically only 30-50% of the total cost of running an agent in production. The rest comes from places you might not have budgeted for.
And API costs themselves can surprise you. Agents tend to be chattier than you'd expect. Tool calls generate additional tokens for the tool description, the parameters, and the response. A task you estimated at 2,000 tokens might actually use 8,000 when you factor in the multi-step tool calling chain.
Infrastructure Overhead
Your agent needs somewhere to run. That's compute cost. It needs MCP servers running and available. That's more compute, plus the infrastructure to keep them healthy. It needs a queue or scheduler if tasks run on a schedule. It needs storage for logs and results. None of this is expensive individually, but it adds up when you're running multiple agents across multiple workflows.
The infrastructure cost scales with reliability requirements. An agent that can be down for an hour without anyone noticing is cheap to run. An agent that needs 99.9% uptime needs redundancy, health checks, automatic restarts, and failover. That's a different cost category entirely.
Monitoring and Observability
You can't run agents in production without monitoring. You need to know when an agent fails, when it's slower than expected, when it produces unexpected outputs, and when the underlying APIs it depends on have issues. Building and maintaining this monitoring infrastructure is a real cost in both engineering time and tooling.
MCP server health monitoring adds another layer. Each server connection needs its own health checks, error rate tracking, and latency monitoring. This isn't hard to set up once, but maintaining it across ten or twenty server connections requires ongoing attention.
Error Handling and Recovery
When an agent fails (and they all do eventually), someone needs to investigate, diagnose, and fix the issue. Sometimes it's a simple retry. Sometimes the agent got confused by unexpected data and made a mess that needs manual cleanup. The time spent on incident response is a real cost that's hard to predict in advance.
Building robust error prevention reduces this cost over time, but it doesn't eliminate it. Novel failure modes appear regularly, especially when external APIs change behavior or data patterns shift.
Maintenance and Evolution
AI agents aren't static. The models they use get updated. The APIs they call change. The requirements they serve evolve. Keeping an agent running well over months requires periodic maintenance: updating prompts, adjusting tool configurations, expanding or contracting the agent's scope. This ongoing maintenance cost is often the biggest surprise for teams that expected "build it and forget it."
None of this means agents aren't worth it. The value they provide usually exceeds the total cost. But budgeting only for API fees and then being surprised by the rest is a pattern worth avoiding.