Cognitive Profiles

Cognitive Profiles are Cerebe's persistent representation of how a user or agent operates. They evolve automatically over time through PLRE analysis and PEOA observation cycles, enabling deeply personalized interactions without manual configuration.

What's in a Profile?

Human Profiles

Built from learning sessions and meta-learning analysis:

Field	Description	Example
`active_phase`	Current PLRE learning phase	`"learn"`
`engagement_level`	Smoothed engagement metric (EWMA)	`0.82`
`cognitive_load`	Current cognitive load estimate	`0.45`
`confidence`	Learner confidence score	`0.71`
`preferred_modalities`	Detected learning modalities	`["visual", "code"]`
`reinforcement_topics`	Topics needing reinforcement	`["recursion", "graphs"]`
`session_count`	Total sessions analyzed	`47`

Agent Profiles

Built from execution traces and PEOA observations:

Field	Description	Example
`task_success_rate`	Smoothed success rate	`0.89`
`execution_efficiency`	Steps-taken vs steps-planned	`0.76`
`plan_coherence_drift`	How much execution diverges from plan	`0.12`
`tool_reliability`	Per-tool success rate and latency	`{"search": {success: 0.95, latency: 120}}`
`preferred_strategies`	Detected strategy patterns	`["depth_first", "verify_then_act"]`

Get a Profile

profile = await client.profiles.get("user_123")

print(profile.engagement_level)     # 0.82
print(profile.preferred_modalities) # ["visual", "code"]
print(profile.active_phase)         # "learn"

const profile = await client.profiles.get('user_123')

console.log(profile.engagementLevel)     // 0.82
console.log(profile.preferredModalities) // ["visual", "code"]
console.log(profile.activePhase)         // "learn"

curl https://api.cerebe.ai/api/v1/meta-learning/profile/user_123 \
  -H "X-API-Key: ck_live_..."

How Profiles Evolve

Profiles update automatically — you don't need to manage them manually.

For humans: Every PLRE analysis cycle updates engagement, confidence, cognitive load, and modality preferences using exponentially weighted moving averages (EWMA). This ensures recent sessions have more influence while maintaining stability.

For agents: Every trace ingestion triggers a PEOA observation that updates task success rate, tool reliability scores, strategy preferences, and plan coherence metrics.

Multi-Agent Coordination

Cognitive profiles support hierarchical memory scoping:

Private — Only the creating agent can read/write
Task-shared — All agents on the same task can read (creator writes)
Org-shared — All agents in the organization can read

This enables teams of agents to share cognitive context without conflicts, with optimistic concurrency control protecting against write races.

API Reference

Method	Endpoint	Description
`GET`	`/api/v1/meta-learning/profile/{user_id}`	Get cognitive profile
`POST`	`/api/v1/meta-learning/plre/transition`	Update PLRE phase
`GET`	`/api/v1/meta-learning/plre/state`	Get current PLRE state
`POST`	`/api/v1/meta-learning/analyze`	Trigger pattern analysis

Next Steps

Meta-Learning — PLRE framework and pattern detection
Agent Traces — Feed execution traces to build agent profiles
Memory Fabric — The memory layer that profiles draw from

Cognitive Profiles

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