Loop Engineering
Design the goal, feedback, verifier, memory, tools, and stop condition instead of hand-steering every prompt.
Why loops break
Loops fail when inference is invisible.
Loop engineering works when a model can run against goals, rubrics, feedback, memory, and verification. But every loop is also an inference workload: prefixes drift, cache reuse collapses, stale evidence fills context, routing gets harder, and serving constraints start to shape the result. Inferoa keeps those tokenmaxxing surfaces inside the harness.
Three words, one runtime
Loop Engineering needs Tokenmaxxing.
Tokenmaxxing
Keep each turn cache-aware, context-bounded, route-conscious, and measurable as the horizon grows.
Inference-native runtime
Expose context windows, prefix cache, model paths, endpoint signals, and serving constraints to the loop.
Proof-oriented loops
Use plans, tests, tool evidence, autoresearch metrics, reflection, and completion reports to decide when to stop.
Mission
Design loops with inference feedback.
Inferoa starts with coding because coding exposes loop pressure clearly: changing goals, tool failures, repeated model calls, context limits, memory needs, verifier signals, and proof through tests. The goal is to co-design the agent harness, goal loop, and inference stack so every turn spends context, cache, route choice, and serving capacity deliberately.
One durable outcome expands through horizons, evidence, reflection, recovery, and completion reports.
Plans, tests, tool results, and autoresearch metrics give the loop concrete feedback to improve against.
Prefix cache, context pressure, routing, multimodal endpoints, and serving constraints stay in the loop.
Quick Look
Inside a Session
01
Welcome
A restrained entry point for the configured model, workspace, and core commands.
02
Goal Mode
Run /goal to start a long-horizon recursive goal with horizons, evidence, and reflection.
03
Plan Mode
Ambiguous scope becomes an inspectable plan before execution starts.
04
Autoresearch
Benchmark runs, failures, fixes, and metrics stay in one research loop.
Built on vLLM Ecosystem
Tokenmaxxing on the vLLM Stack
High-performance serving is the base. inferoa treats prefix-cache stability and endpoint signals as agent state.
Routing belongs in the loop. Cost, safety, privacy, capability, and session pressure can choose the model path.
Multimodal work stays native. Image, video, and audio understanding or generation live in the same durable session.
Cross-stack path
Across the Tokenmaxxing Stack
- 01Goal Looprecursive horizons + reflection→
- 02Agent Harnesssessions, tools, evidence→
- 03Tokenmaxxingprefix, context, routing→
- 04vLLM ServingEngine + Omni