XIII · Cognition & IntrospectionEmerging★

Awareness

also known as Situational Awareness, Capability Self-Knowledge

Maintain the agent's explicit knowledge of its own tools, capabilities, environment, and current context as queryable state.

Context

A team is building an agent that operates across multiple sessions and whose set of available tools, permissions, and roles changes at runtime. The agent needs to reason about what it can actually do right now — which tools are wired in, which are disabled, who the current user is, which permissions apply — rather than relying on whatever the original system prompt happened to mention. Without an explicit place where this information lives, capability is buried implicitly in prompt text and stale the moment anything changes.

Problem

An agent that has no reliable picture of its own current capabilities fails in two predictable directions. It promises to invoke tools it does not actually have, fabricating plausible function calls that error out at dispatch. Or it forgets that it does have a particular tool and falls back on weaker workarounds when the right capability was available all along. Both failure modes are invisible to the model because nothing in its context tells it what is really wired up at this moment.

Forces

  • Awareness state grows with capability.
  • Stale awareness misleads.
  • Self-description is itself a prompt-engineering effort.

Example

A field-service agent occasionally promises to 'check the parts inventory' even though that tool was disabled in the latest deploy, then apologises when the call fails. The root cause is that the agent has no reliable picture of what it actually has. The team adds an Awareness module that exposes tool names, descriptions, and current health as queryable state the agent reads each turn. Now when the inventory tool is offline, the agent sees that fact in its own context and offers an alternative instead of fabricating one.

Diagram

Solution

Therefore:

Persist explicit state about: available tools (with descriptions), the environment (what host, what user, what permissions), the current task, and the agent's own identity. Refresh on capability changes. Inject relevant slices of awareness into each turn's context.

What this pattern forbids. Tool calls and self-references must match the awareness state; mismatches are flagged.

The smaller patterns that complete this one —

  • generalisesNow-Anchoring·— Ground the agent's reasoning in the current absolute time without requiring tool calls, so every reply is implicitly time-aware.
  • generalisesReflexive Metacognitive Agent·— Agent maintains an explicit self-model of its own capabilities, confidence and limitations, and reasons over that model when accepting / refusing / handing off tasks.

And the patterns that stand alongside it, or against it —

  • complementsTool Use★★— Let the LLM produce typed calls against an external toolkit instead of producing free-form text the surrounding system has to parse.
  • complementsTool Discovery★— Let the agent discover available tools at runtime rather than hardcoding the tool list at agent build time.
  • complementsLiminal-State Detection·— Infer the human's attentional state (just-woke, focused, winding-down, distracted) from message timing and tone, and adapt response shape so the agent meets the person where they actually are.
  • complementsEmbodied-Proxy Handoff·— Enable the human to share embodied state (energy, fatigue, environment) so the agent tailors response shape to the actual person rather than to a context-free abstract user.
  • complementsCo-Located Memory Surfacing·— Surface relevant persistent memories proactively when the human mentions a concrete entity the agent has prior knowledge of, so the human does not bear the burden of remembering to ask.
  • alternative-toMemo-As-Source Confusion★— Anti-pattern: the agent cites its own past memos as ground truth instead of re-verifying them against the artifacts they describe, accumulating false confidence in stale summaries.
  • complementsPreoccupation Tracking★— Maintain a small set of mid-term, affect-tagged concerns that persist across days and surface in every prompt, distinct from the single-item working focus and from long-term insights.
  • complementsEmotional State Persistence★— Track the agent's affective state as bounded, decaying scalars across ticks so reasoning can react to its own emotional load instead of treating each turn as emotionally blank.
  • complementsWorld-Model Separation★— Maintain an explicit, surprise-updated model of the environment (humans, repos, services, capabilities) in a separate file from the agent's self-model, so the two cannot be confused or co-mutated by reflection.
  • complementsSubject-First Agent Architecture (ENA Stateful Core)·— Invert the LLM-centric pipeline: the agent is a stateful subject whose decision logic chooses whether to invoke the LLM at all, treating the model as one tool among many.

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Used in frameworks

References

Provenance