Cost management and governance strategies
Prerequisites: network-topographies-egress-control
Autonomous agents can be expensive: every reasoning step may call an LLM, and volume grows fast. This closing lesson covers the cost drivers of an agent platform and the governance strategies - many of which you already have - to keep spend predictable.
What you will learn
- The main cost drivers in an agent platform.
- Governance strategies to bound and attribute cost.
- How existing Omni Gateway policies double as cost controls.
Cost drivers
- LLM tokens - usually the largest variable cost; grows with prompt size, retrieved context, and number of reasoning steps.
- Runtime capacity - replicas and compute for deployed agents (see Track 4).
- Call volume - A2A and MCP calls fan out as networks grow (visible via the Observe metrics from Track 4).
Governance strategies
- Bound usage with Rate Limiting and Spike Control policies at the Omni Gateway so runaway loops cannot run up unbounded cost (Included Policies).
- Attribute cost by tracking A2A/MCP call metrics per agent (Track 4’s Agent Visualizer) so teams own their consumption.
- Right-size context - the context-optimizer discipline from Track 1: fewer, better tokens cost less and reason better.
Your governance controls are cost controls
You have built the platform
Across five tracks you moved from AI-native fundamentals to a governed, observable, cost-controlled agent platform - practicing all four MAF capabilities: Discover, Govern, Orchestrate, and Observe.
Where to go next
You can build a governed agent network; the final track proves it keeps working. Continue to Track 6: Testing & Assurance to test agentic systems the right way - layered regression, golden datasets, A2A mocks, the test pyramid, and beyond-regression conformance, graph, auth, and governance suites. Or revisit any track from the home page and explore lessons by capability on the capability view.