Inference-Aware AI: Working Definitions

In my last post, I introduced the hypothesis that AI agents could become faster, cheaper, and more effective if they were inference-aware — meaning they could adapt their thinking and actions based on cost, quality needs, and context.

This post is a working glossary of the terms and concepts I am using in this space. The goal is to give us a shared language as I explore whether this idea has long-term value.

Core Terms

Model Call

A single request to an AI model. This could be a short question to a chatbot or a large batch of data for processing. Each call has a cost, both in money and time.

Token

The unit of text (or data) that AI models process. Think of it as a “word chunk” — models charge by the number of tokens they read and write. More tokens means higher cost and longer processing time.

Reasoning Step

One pass of “thinking” by an agent. Some agents take many steps to solve a problem; others can answer in one. More steps can mean better answers, but also more expense.

Tool Call

When an agent uses a piece of software, database, or API instead of (or in addition to) calling an AI model. Example: checking the system clock for the date instead of asking an LLM.

Escalation

Switching to a more capable (and usually more expensive) model or resource when the current one is not producing a good enough result.

Inference

The act of running an AI model to get an output.
Training teaches the model. Inference is using the trained model to answer a question, process data, or make a decision.
Example: Calling an LLM to summarize a paragraph.

Aware

The ability for an agent to understand its task, the resources available, the cost of different options, and when to change its approach. Awareness means the agent is not blindly following instructions, but actively optimizing.

Agents

Autonomous software workers that can perceive information, decide on a course of action, and act — often in a loop. Agents can work alone or in teams, with or without memory, and may use tools and APIs to complete tasks.

Platform

The environment where agents operate, share memory, use tools, and follow rules. A platform includes infrastructure for task routing, cost control, communication, and governance.

Types of Agents

• Single-task agents: Do one job and forget it.
• Multi-step agents: Break large tasks into smaller parts and plan execution.
• Tool-using agents: Combine reasoning with calls to APIs, databases, or software tools.
• Memory-enabled agents: Retain context and history for better future decisions.
• Collaborative agents: Teams of specialized agents working together.
• Autonomy-driven agents: Self-starting agents that act without being directly prompted.

Awareness Dimensions

• Task Awareness: Knows the goal, quality requirements, and urgency.
• Resource Awareness: Knows time, compute, and budget constraints.
• Model Awareness: Understands model strengths, weaknesses, and costs.
• Tool Awareness: Knows when to use tools instead of more model reasoning.
• Context Awareness: Remembers prior work to avoid repeating effort.
• Self-Performance Awareness: Knows historical accuracy and reliability.

Platform Components

The Roads – Execution and Routing Infrastructure

How tasks and data move between agents, models, and tools. Includes scheduling, scaling, and model selection.

The Rules – Governance and Decision Policies

Budgets, quality thresholds, permissions, and escalation criteria for agents.

The Utilities – Shared Services and Capabilities

Memory, knowledge bases, tool libraries, logging, tracing, and test environments.

The Communication – Protocols and Coordination

Messaging systems, context passing, negotiation, and human-in-the-loop channels.