Agent efficiency is the layer that keeps agent systems from paying for the same context over and over. It covers how much context gets loaded, how that context is compressed, when the system discovers more only if needed, and which model is worth the spend for that step.
That matters long before a buyer looks at a model bill. If every request drags too much context, too many heavy models, and too much duplicate retrieval, cost rises fast and behavior gets noisier. We tighten that operating layer so the system stays lighter, cheaper, and easier to scale without cutting the meaning out of the work.
Cost climbs when every request drags the whole system with it
Many agent setups waste money before the team notices. One workflow loads a full account record when it needs two fields. Another pushes verbose JSON through every step. A third calls the heavier model by default because nobody designed a lighter path first. The result is familiar: slower runs, higher bills, and systems that become harder to reason about once usage grows.
Compression only works when structure survives the squeeze
Smaller context is useful only when it stays trustworthy. We have worked with schema-backed payloads, compact context formats, and Toon-style internal conventions that shrink token usage without turning the payload into folklore. That usually means one source of truth for the schema, encoders and decoders with round-trip tests, explicit versioning, and lint rules that stop teams from slipping back into ad hoc blobs.
Progressive discovery beats loading everything up front
The cheapest context is often the context you never loaded. We use progressive discovery when an agent can start with a smaller view, then ask for more only when the task truly needs it. That keeps prompts shorter, retrieval tighter, and system behavior easier to inspect. It also lowers the risk that one bloated context bundle becomes the default answer to every problem.
Model usage needs routing, not habit
Efficiency is not only about compression. It is also about where the expensive model is justified and where it is not. We have worked with lighter-first model selection, escalation rules for harder reasoning steps, and boundaries that keep classification, extraction, and lookup work from always landing on the most expensive path. That is where real cost control starts becoming operational instead of theoretical.
Strong fit, weak fit
The strongest fit is a team already running agent workflows and feeling the cost, latency, or context sprawl that comes from loading too much and routing poorly. The weak fit is a team still proving the workflow at all. If nothing is stable yet, heavy efficiency work is early. But once the system is real, this layer usually pays for itself quickly.
