ARCHER Suite: Use Cases, Interaction Architecture, and Differentiation Analysis

Document Purpose

This document provides:

Extracted use cases from team discussions for ODIE, Cogniscient, and ARCHER development and testing
Detailed interaction diagrams showing component collaboration
Differentiation analysis comparing ARCHER to traditional RL, offline LLMs, RAG, and Graph RAG
Component input/output specifications for implementation guidance

Created: February 2026
Status: Development Reference

Part 1: Use Cases for Development and Testing

1.1 Use Cases from Team Discussion (Feb 2, 2026)

The following use cases were explicitly or implicitly discussed during the team standup:

Use Case 1: SQL Query Writing and Execution

Source: Discussion about LLMs struggling with complex queries

Scenario: User asks Archer to retrieve data from a Postgres database.

The Problem Being Solved:

LLMs can write simple queries but fail at complex ones
Repeated queries shouldn't require re-generation each time
Database context (schema, relationships) must persist

Component Roles:

Component	Role
Archer	Receives natural language request, orchestrates response
ODIE	Evaluates "minimize time to get accurate data" outcome
Cogniscient	Stores schema knowledge, successful query patterns, user preferences
Librarian	Selects appropriate query builder tool based on database type
Fluxio	Executes the query, returns results

Success Criteria:

Query executes correctly on first attempt
Subsequent similar queries are faster (pattern memory)
Complex joins and aggregations produce correct results
System remembers which approaches worked vs. failed

Use Case 2: Librarian Tool Selection

Source: Discussion about finding the right tool for the job

Scenario: System needs to select from multiple query builder tools (Postgres, Neo4j, SQL Server, etc.)

The Problem Being Solved:

Multiple tools exist for similar purposes
Selection depends on context (database type, query complexity, user history)
The "best" tool may change based on accumulated experience

Component Roles:

Component	Role
Librarian	Maintains tool registry, scores tools by context, recommends optimal choice
Cogniscient	Provides historical performance data for tools in similar contexts
ODIE	Weights tool selection by outcome importance (accuracy vs. speed vs. cost)
Archer	Presents recommendation, handles user override

Differentiation: Unlike static tool registries, Librarian learns which tools succeed in which contexts and improves recommendations over time.

Use Case 3: Memory Decay and Relevance

Source: Discussion about "wreck diving" example - irrelevant old interests polluting current context

Scenario: User interested in topic A twenty years ago, now interested in topic B. System should not surface A when querying for B-related information.

The Problem Being Solved:

Traditional RAG retrieves by semantic similarity alone
Old, irrelevant information pollutes current context
No mechanism for "forgetting" or deprioritizing stale knowledge

Component Roles:

Component	Role
Cogniscient	Applies temporal decay to memory strength scores
ODIE	Filters retrieval by currently active outcomes (outcome-aligned retrieval)
Archer	Ensures returned context is relevant to current user intent

Differentiation: ARCHER's memory retrieval is outcome-weighted, not just similarity-based. Memories that contributed to successful outcomes in similar contexts are reinforced; irrelevant memories decay.

Use Case 4: Conceptual vs. Text-Based Search

Source: Discussion about chunking and "not just a keyword search"

Scenario: User asks about a topic discussed across multiple conversations. System should retrieve the conceptual thread, not isolated text fragments.

The Problem Being Solved:

RAG retrieves chunks by text similarity
Concepts span multiple documents/conversations
The "idea" being discussed may use different words in different contexts

Component Roles:

Component	Role
Cogniscient	Maintains concept graph linking related memories by meaning, not just text
ODIE	Activates related outcomes to expand retrieval scope
Archer	Synthesizes retrieved fragments into coherent response

Differentiation: Cogniscient stores concepts and relationships, not just embedded text chunks. Retrieval can traverse conceptual links to find related information even when the words differ.

Use Case 5: Continuous Learning from Feedback

Source: Discussion about "myelination" - reinforcing pathways that produce good results

Scenario: Action produces outcome. If outcome matches or exceeds expectation, reinforce the pattern. If it fails, weaken the pattern.

The Problem Being Solved:

Traditional systems don't learn from their own outputs
No feedback loop between action and outcome
Same mistakes repeated indefinitely

Component Roles:

Component	Role
Fluxio	Executes action, reports result
ODIE	Compares expected outcome delta vs. actual delta
Cogniscient	Updates memory strength scores based on success/failure
Librarian	Adjusts tool scores based on execution feedback

Differentiation: This is the continuous learning loop that makes ARCHER fundamentally different from static systems. The system improves through use.

Use Case 6: Cross-Client Data Isolation

Source: Discussion about proposal writing pulling "another client's stuff"

Scenario: Writing a proposal for Client A. System must not surface information from Client B's projects.

The Problem Being Solved:

Memory systems can leak context across domains
Regulated industries require strict data isolation
User shouldn't need to manually filter results

Component Roles:

Component	Role
Cogniscient	Maintains tenant/project scoping on all memories
ODIE	Activates only outcomes relevant to current context
Archer	Enforces governance policies before surfacing information

Use Case 7: Transcript Processing and Concept Extraction

Source: Discussion about chunking transcripts by concept, not just time

Scenario: A two-hour meeting transcript needs to be processed for memory storage.

The Problem Being Solved:

Raw chunking loses conceptual coherence
Important ideas may be scattered across the timeline
Summary alone loses detail; full text loses signal

Component Roles:

Component	Role
Cogniscient	Extracts concepts, entities, relationships from transcript
ODIE	Identifies which concepts relate to active outcomes
Archer	Enables querying by concept across transcripts

Differentiation: Chunks are conceptual, not temporal. Related statements from different points in the conversation are linked together.

1.2 Additional Use Cases for Comprehensive Testing

Use Case 8: NPC Motivation System (DM Weaver)

Context: AI NPCs in gaming need believable, persistent motivation

Scenario: NPC shopkeeper encounters player with stolen goods. Must balance:

Outcome: Acquire gold for shop
Outcome: Maintain village reputation
Constraint: Avoid thieves' guild conflict

Component Roles:

Component	Role
ODIE	Evaluates action hypotheses against weighted outcomes
Cogniscient	Recalls NPC's relationship history with player and guild
Fluxio	Executes NPC's chosen behavior
Archer	Maintains narrative coherence across interactions

Use Case 9: Enterprise Support Ticket Optimization

Context: Customer support system optimizing for resolution outcomes

Scenario: Customer calls with support issue. System must:

Minimize time to reach agent
Minimize time to resolution
Maximize first-call resolution rate
Minimize customer effort

Component Roles:

Component	Role
ODIE	Detects rising wait times, flags outcome regression
Cogniscient	Recalls similar issues, successful resolution patterns
Librarian	Recommends appropriate escalation path or self-service option
Archer	Coordinates human agent and automated responses

Use Case 10: Belief Revision Under New Evidence

Context: System holds belief based on previous evidence; new contradicting evidence arrives

Scenario: System believes "Vendor X has 30-day payment terms" based on old contract. New contract shows 45-day terms.

Component Roles:

Component	Role
ODIE	Detects conflicting evidence, triggers belief revision
Cogniscient	Updates belief confidence, maintains genealogy of belief changes
Archer	Surfaces the change to relevant users

1.3 Cogniscient-Specific Use Cases

Use Case C1: Experience-Based Memory Formation

Scenario: Store a memory with contextual metadata (emotional weight, active outcomes, formation context)

Inputs:

Raw content (text, structured data)
Active outcome pool at formation time
Emotional markers (if applicable)
Relationship links to existing entities

Outputs:

Memory ID
Embedding vector
Concept assignments
Relationship edges

Use Case C2: Outcome-Weighted Retrieval

Scenario: Retrieve memories relevant to both a query and the current active outcome pool

Inputs:

Query text
Active outcome pool with weights
Retrieval parameters (top-k, recency weight)

Outputs:

Ranked memory list
Per-memory relevance scores (semantic + outcome alignment)
Formation context for each memory

Use Case C3: Supersession Chain Tracking

Scenario: Track how a belief or fact evolved over time

Inputs:

Entity or concept ID
Time range (optional)

Outputs:

Chain of states with timestamps
Evidence that triggered each change
Confidence at each state

Use Case C4: Associative Leap Retrieval

Scenario: Find memories that are semantically distant but conceptually linked

Inputs:

Query text
Association depth (number of hops)

Outputs:

Direct matches (semantic similarity)
Leap matches (connected via concept graph)
Path explanation for leap matches

Part 2: Differentiation Analysis

2.1 ARCHER vs. Traditional Reinforcement Learning

Dimension	Traditional RL	ARCHER/ODIE
Learning Signal	Reward function (scalar)	Outcome delta across multiple dimensions
Goal Definition	Implicit in reward design	Explicit outcomes with importance/satisfaction
State Representation	Fixed observation space	Dynamic, context-dependent outcome pool
Exploration	Random or policy-based	Intent-guided by active outcomes
Memory	Replay buffer (uniform or prioritized)	Outcome-weighted retrieval with decay
Adaptability	Requires retraining for new goals	New outcomes added without retraining
Explainability	Opaque value functions	Traceable outcome-action-delta chains
Human Integration	Reward shaping	Direct outcome and constraint specification

Key Insight: RL learns what to do through trial-and-error reward maximization. ODIE knows why through explicit outcome definitions and learns how through action-outcome feedback.

2.2 ARCHER vs. Offline LLMs

Dimension	Offline LLM	ARCHER
Knowledge	Static (training cutoff)	Dynamic (continuous updates)
Context Window	Fixed size, session-bound	Persistent, outcome-weighted retrieval
Learning	None post-training	Continuous through feedback loops
Personalization	None or prompt-based	Deep personalization via Cogniscient
Goal Orientation	Task completion	Outcome achievement
Memory	None between sessions	Persistent, evolving, strengthening/decaying
Judgment	Pattern matching	Explicit reasoning against outcomes

Key Insight: Offline LLMs are stateless pattern matchers. ARCHER maintains persistent state, learns from its actions, and reasons about outcomes.

2.3 ARCHER vs. Standard RAG

Dimension	Standard RAG	ARCHER
Retrieval Basis	Semantic similarity only	Semantic + outcome alignment + recency + reinforcement
Index Structure	Flat vector store	Dual-graph (similarity + concept association)
Chunk Strategy	Fixed-size or sentence-based	Conceptual chunking with relationship preservation
Learning	None	Retrieval improves through use (myelination)
Context Selection	Top-k by similarity	Outcome-weighted ranking
Temporal Awareness	None or minimal	Decay, recency, supersession tracking
Cross-Domain	Retrieves any similar content	Outcome pooling filters by relevance

Key Insight: RAG answers "what text is similar?" ARCHER answers "what information serves my current outcomes?"

2.4 ARCHER vs. Graph RAG / Knowledge Graphs

Dimension	Graph RAG	ARCHER/Cogniscient
Graph Purpose	Entity relationships	Concept associations + experience links
Node Types	Entities, facts	Memories, concepts, outcomes, beliefs
Edge Types	Typed relationships	Association strength (learned), outcome impact
Retrieval	Graph traversal for facts	Dual-path: similarity space + association space
Evolution	Manual or rule-based updates	Hebbian learning, decay, reinforcement
Emotional Dimension	None	Full emotional tagging and congruent retrieval
Outcome Integration	Separate	Native (outcome-weighted retrieval)

Key Insight: Graph RAG structures facts. Cogniscient structures experiences and learns from them.

2.5 What Makes ODIE Unique

ODIE addresses a gap no current system handles:

The Problem

Current AI systems optimize for:

Task completion (did I do what was asked?)
Accuracy (is my answer correct?)
User satisfaction (thumbs up/down)

They do NOT optimize for:

Outcome achievement (did the user get what they actually needed?)
Outcome movement (did we make progress toward stated goals?)
Multi-objective balancing (which outcomes should be prioritized?)

ODIE's Contribution

Outcomes as First-Class Objects
- Not KPIs, metrics, or rewards
- Structured: Direction + Measure + Object + Context
- Weighted by importance and satisfaction
Dynamic Outcome Pooling
- Not all outcomes evaluated for all decisions
- Context-dependent activation via signals, hierarchy, graph proximity
- Exceeds human bounded rationality without brute-force evaluation
Intent as Emergent Property
- Intent is not a separate object or input
- Intent emerges from the weighted outcome pool for each decision
- Computed fresh each cycle, never stale
Continuous Feedback Loop
- Expected delta vs. actual delta comparison
- Satisfaction scores update automatically
- Retrieval quality improves through outcome correlation
Belief Revision Under Uncertainty
- Beliefs carry confidence scores
- Evidence updates confidence via Bayesian inference
- Belief changes propagate to affected outcomes

What Nobody Else Is Building

The combination of:

Explicit outcome definitions (from ODI/JTBD methodology)
Dynamic outcome pooling (solving the combinatorial challenge)
Outcome-weighted memory retrieval (connecting ODIE to Cogniscient)
Continuous learning from action-outcome feedback (closing the loop)

This is outcome-driven cognition, not reward-driven learning or similarity-driven retrieval.

Part 3: Component Interaction Architecture

3.1 High-Level System Flow

┌─────────────────────────────────────────────────────────────────────────────┐
│                            ARCHER SUITE                                      │
│                                                                             │
│  ┌─────────────┐                                                            │
│  │   USER /    │                                                            │
│  │  EXTERNAL   │─────────────┐                                              │
│  │   SYSTEM    │             │                                              │
│  └─────────────┘             ▼                                              │
│                    ┌─────────────────────┐                                  │
│                    │   ARCHER            │                                  │
│                    │   ORCHESTRATOR      │                                  │
│                    │   (UX Layer)        │                                  │
│                    └────────┬────────────┘                                  │
│                             │                                               │
│           ┌─────────────────┼─────────────────┐                             │
│           │                 │                 │                             │
│           ▼                 ▼                 ▼                             │
│  ┌─────────────┐   ┌─────────────┐   ┌─────────────┐                       │
│  │ COGNISCIENT │   │    ODIE     │   │  LIBRARIAN  │                       │
│  │ (Memory)    │◄─►│ (Reasoning) │◄─►│ (Resources) │                       │
│  └──────┬──────┘   └──────┬──────┘   └──────┬──────┘                       │
│         │                 │                 │                               │
│         └─────────────────┼─────────────────┘                               │
│                           │                                                 │
│                           ▼                                                 │
│                    ┌─────────────────────┐                                  │
│                    │      FLUXIO         │                                  │
│                    │  (Execution Runtime)│                                  │
│                    └─────────────────────┘                                  │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

3.2 Detailed Decision Cycle Interaction

Phase 1: SENSE (Signal Ingestion)

┌─────────────┐    ┌──────────────┐    ┌────────────┐    ┌─────────────┐
│ ENVIRONMENT │    │    ARCHER    │    │    ODIE    │    │ COGNISCIENT │
│  (Signals)  │    │              │    │            │    │             │
└──────┬──────┘    └──────┬───────┘    └─────┬──────┘    └──────┬──────┘
       │                  │                  │                  │
       │  Signal Event    │                  │                  │
       │─────────────────►│                  │                  │
       │                  │                  │                  │
       │                  │  Forward Signal  │                  │
       │                  │─────────────────►│                  │
       │                  │                  │                  │
       │                  │                  │  Query Context   │
       │                  │                  │─────────────────►│
       │                  │                  │                  │
       │                  │                  │  Return Context  │
       │                  │                  │◄─────────────────│
       │                  │                  │                  │
       │                  │                  │  Classify Signal │
       │                  │                  │  Link to Outcomes│
       │                  │                  │                  │

Data Exchanged:

From	To	Data	Purpose
Environment	Archer	Signal (type, source, content, timestamp)	Trigger processing
Archer	ODIE	Signal + User context	Begin reasoning
ODIE	Cogniscient	Context query	Retrieve relevant history
Cogniscient	ODIE	Historical context, entity relationships	Ground signal interpretation

Phase 2: POOL (Outcome Pool Construction)

┌──────────────┐    ┌────────────┐    ┌─────────────┐
│    ODIE      │    │ COGNISCIENT│    │   OUTCOME   │
│              │    │            │    │    GRAPH    │
└──────┬───────┘    └─────┬──────┘    └──────┬──────┘
       │                  │                  │
       │  Identify Direct │                  │
       │  Outcome(s)      │                  │
       │                  │                  │
       │  Expand via      │                  │
       │  Hierarchy       │─────────────────►│
       │                  │                  │
       │                  │  Parent/Siblings │
       │                  │◄─────────────────│
       │                  │                  │
       │  Expand via      │                  │
       │  Signal Activation                  │
       │─────────────────►│                  │
       │                  │                  │
       │  Active Outcomes │                  │
       │◄─────────────────│                  │
       │                  │                  │
       │  Expand via      │                  │
       │  Graph Proximity │─────────────────►│
       │                  │                  │
       │  Proximate       │                  │
       │  Outcomes        │                  │
       │◄─────────────────│─────────────────►│
       │                  │                  │
       │  ACTIVE POOL     │                  │
       │  CONSTRUCTED     │                  │
       │                  │                  │

Data Exchanged:

From	To	Data	Purpose
ODIE	Outcome Graph	Direct outcome ID	Start pool construction
Outcome Graph	ODIE	Hierarchical relatives (parent, siblings)	Expand scope
ODIE	Cogniscient	Active signal IDs	Find signal-linked outcomes
Cogniscient	ODIE	Signal-activated outcome IDs	Expand scope
ODIE	Outcome Graph	Current pool, proximity depth	Graph expansion
Outcome Graph	ODIE	Proximate outcomes with distance weights	Complete pool

Output: Active Outcome Pool

{
  "pool": [
    {"outcome_id": "OUT-001", "weight": 0.85, "source": "direct"},
    {"outcome_id": "OUT-002", "weight": 0.65, "source": "hierarchy"},
    {"outcome_id": "OUT-003", "weight": 0.45, "source": "signal_activated"},
    {"outcome_id": "OUT-004", "weight": 0.30, "source": "graph_proximity"}
  ],
  "total_weight": 2.25,
  "construction_method": "hierarchical+signal+proximity"
}

Phase 3: SCORE (Action Hypothesis Evaluation)

┌──────────────┐    ┌────────────┐    ┌─────────────┐
│    ODIE      │    │  LIBRARIAN │    │ COGNISCIENT │
│              │    │            │    │             │
└──────┬───────┘    └─────┬──────┘    └──────┬──────┘
       │                  │                  │
       │  Request Action  │                  │
       │  Hypotheses      │                  │
       │─────────────────►│                  │
       │                  │                  │
       │  Action          │                  │
       │  Candidates      │                  │
       │◄─────────────────│                  │
       │                  │                  │
       │  Request         │                  │
       │  Relevant Memory │─────────────────►│
       │                  │                  │
       │  Outcome-Aligned │                  │
       │  Memories        │                  │
       │◄─────────────────│─────────────────►│
       │                  │                  │
       │  For each action:│                  │
       │  MAUT Score =    │                  │
       │  Σ(wᵢ × δᵢ)     │                  │
       │                  │                  │
       │  RANKED ACTIONS  │                  │
       │                  │                  │

Data Exchanged:

From	To	Data	Purpose
ODIE	Librarian	Signal context, outcome pool	Generate action candidates
Librarian	ODIE	Ranked action hypotheses with expected deltas	Evaluation input
ODIE	Cogniscient	Query + Active outcome pool	Outcome-weighted retrieval
Cogniscient	ODIE	Memories with relevance scores	Inform delta estimation

Scoring Formula:

U(action) = Σ [ wᵢ × expected_delta(action, outcomeᵢ) ]

where:
  wᵢ = (2 × importance) - satisfaction  (opportunity score)
  expected_delta = estimated outcome movement from action

Phase 4: FILTER (Constraint Application)

┌──────────────┐    ┌─────────────────┐
│    ODIE      │    │   CONSTRAINT    │
│              │    │   REGISTRY      │
└──────┬───────┘    └────────┬────────┘
       │                     │
       │  Check Hard         │
       │  Constraints        │
       │────────────────────►│
       │                     │
       │  Violations         │
       │◄────────────────────│
       │                     │
       │  Eliminate          │
       │  Violating Actions  │
       │                     │
       │  Check Soft         │
       │  Constraints        │
       │────────────────────►│
       │                     │
       │  Tensions           │
       │◄────────────────────│
       │                     │
       │  Add Tension        │
       │  Outcomes to Pool   │
       │  Rescore            │
       │                     │

Data Exchanged:

From	To	Data	Purpose
ODIE	Constraint Registry	Action hypotheses	Check violations
Constraint Registry	ODIE	Hard violations (eliminate)	Remove invalid actions
Constraint Registry	ODIE	Soft tensions (with associated outcomes)	Expand scoring

Phase 5: SELECT and EXECUTE

┌──────────────┐    ┌────────────┐    ┌─────────────┐    ┌─────────────┐
│    ODIE      │    │   ARCHER   │    │   FLUXIO    │    │   TOOL      │
│              │    │            │    │             │    │             │
└──────┬───────┘    └─────┬──────┘    └──────┬──────┘    └──────┬──────┘
       │                  │                  │                  │
       │  Top Action(s)   │                  │                  │
       │─────────────────►│                  │                  │
       │                  │                  │                  │
       │                  │  Present to User │                  │
       │                  │  (if approval    │                  │
       │                  │   required)      │                  │
       │                  │                  │                  │
       │                  │  Execute Action  │                  │
       │                  │─────────────────►│                  │
       │                  │                  │                  │
       │                  │                  │  Invoke Tool     │
       │                  │                  │─────────────────►│
       │                  │                  │                  │
       │                  │                  │  Tool Result     │
       │                  │                  │◄─────────────────│
       │                  │                  │                  │
       │                  │  Execution       │                  │
       │                  │  Complete        │                  │
       │                  │◄─────────────────│                  │
       │                  │                  │                  │

Data Exchanged:

From	To	Data	Purpose
ODIE	Archer	Ranked action(s), expected deltas, reasoning	Present/execute
Archer	Fluxio	Action specification, parameters	Execute
Fluxio	Tool	Tool-specific input	Perform action
Tool	Fluxio	Execution result, status	Capture outcome
Fluxio	Archer	Execution record	Report completion

Phase 6: OBSERVE and UPDATE (Learning Loop)

┌──────────────┐    ┌────────────┐    ┌─────────────┐    ┌─────────────┐
│   FLUXIO     │    │    ODIE    │    │ COGNISCIENT │    │  LIBRARIAN  │
│              │    │            │    │             │    │             │
└──────┬───────┘    └─────┬──────┘    └──────┬──────┘    └──────┬──────┘
       │                  │                  │                  │
       │  Execution       │                  │                  │
       │  Result          │                  │                  │
       │─────────────────►│                  │                  │
       │                  │                  │                  │
       │                  │  Compare:        │                  │
       │                  │  expected_delta  │                  │
       │                  │  vs actual_delta │                  │
       │                  │                  │                  │
       │                  │  Update          │                  │
       │                  │  Satisfaction    │                  │
       │                  │  Scores          │                  │
       │                  │                  │                  │
       │                  │  Memory          │                  │
       │                  │  Reinforcement   │                  │
       │                  │─────────────────►│                  │
       │                  │                  │                  │
       │                  │                  │  Update          │
       │                  │                  │  Strength        │
       │                  │                  │  Scores          │
       │                  │                  │                  │
       │                  │  Tool            │                  │
       │                  │  Feedback        │                  │
       │                  │─────────────────►│─────────────────►│
       │                  │                  │                  │
       │                  │                  │                  │  Update
       │                  │                  │                  │  Tool
       │                  │                  │                  │  Scores
       │                  │                  │                  │
       │                  │  Store           │                  │
       │                  │  Experience      │                  │
       │                  │─────────────────►│                  │
       │                  │                  │                  │
       │                  │                  │  Create Memory   │
       │                  │                  │  with Outcome    │
       │                  │                  │  Tags            │
       │                  │                  │                  │

Data Exchanged:

From	To	Data	Purpose
Fluxio	ODIE	Execution result, observed signals	Calculate actual delta
ODIE	Cogniscient	Memory IDs, delta comparison	Reinforce/weaken memories
ODIE	Librarian	Tool ID, success/failure	Update tool scores
ODIE	Cogniscient	Experience record (action + context + outcome + delta)	Store new memory

Feedback Calculation:

delta_match = actual_delta / expected_delta

if delta_match >= 1.0:
    # Success - reinforce
    memory.strength += reinforcement_rate * delta_match
    tool.score += tool_learning_rate * delta_match
else:
    # Underperformance - weaken
    memory.strength -= decay_rate * (1 - delta_match)
    tool.score -= tool_learning_rate * (1 - delta_match)

3.3 Complete Decision Cycle Swim Lane

┌────────────┬──────────────┬──────────────┬──────────────┬──────────────┬──────────────┐
│ENVIRONMENT │    ARCHER    │     ODIE     │ COGNISCIENT  │  LIBRARIAN   │   FLUXIO     │
│            │ Orchestrator │  Reasoning   │   Memory     │  Resources   │  Execution   │
├────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┤
│            │              │              │              │              │              │
│ Signal     │              │              │              │              │              │
│ ──────────►│ SENSE        │              │              │              │              │
│            │ ────────────►│ Receive      │              │              │              │
│            │              │ ────────────►│ Context      │              │              │
│            │              │ ◄────────────│ Query        │              │              │
│            │              │              │              │              │              │
│            │              │ POOL         │              │              │              │
│            │              │ Build active │              │              │              │
│            │              │ outcome pool │              │              │              │
│            │              │ ────────────►│ Signal       │              │              │
│            │              │ ◄────────────│ Activation   │              │              │
│            │              │              │              │              │              │
│            │              │ SCORE        │              │              │              │
│            │              │ ────────────►│              │ ────────────►│              │
│            │              │ ◄────────────│ Memories     │ Candidates   │              │
│            │              │ ◄────────────│              │ ◄────────────│              │
│            │              │              │              │              │              │
│            │              │ FILTER       │              │              │              │
│            │              │ Check        │              │              │              │
│            │              │ constraints  │              │              │              │
│            │              │              │              │              │              │
│            │              │ SELECT       │              │              │              │
│            │ ◄────────────│ Top action   │              │              │              │
│            │              │              │              │              │              │
│            │ ACT          │              │              │              │              │
│            │ ─────────────│──────────────│──────────────│─────────────►│ Execute      │
│            │              │              │              │              │ ────────────►│
│            │ ◄────────────│──────────────│──────────────│──────────────│ Result       │
│            │              │              │              │              │              │
│            │              │ OBSERVE      │              │              │              │
│            │              │ ◄────────────│──────────────│──────────────│ Feedback     │
│            │              │              │              │              │              │
│            │              │ UPDATE       │              │              │              │
│            │              │ ────────────►│ Reinforce    │              │              │
│            │              │ ────────────►│              │ ────────────►│              │
│            │              │              │ Store        │ Tool scores  │              │
│            │              │              │ Experience   │              │              │
│            │              │              │              │              │              │
│            │              │ ◄─── LOOP ───│──────────────│──────────────│──────────────│
│            │              │              │              │              │              │
└────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┘

Part 4: Component Input/Output Specifications

4.1 Archer Orchestrator

Inputs

Input	Source	Format	Purpose
User Request	UI/API	Natural language or structured	Trigger processing
Signal Event	Connected Systems	Signal object (type, source, content)	Environmental awareness
Execution Result	Fluxio	Result object (status, output, duration)	Complete action cycle
Policy Context	Governance Layer	Permission set, approval requirements	Enforce boundaries

Outputs

Output	Destination	Format	Purpose
Context Query	Cogniscient	Query object (content, filters)	Retrieve relevant memory
Reasoning Request	ODIE	Decision context object	Trigger reasoning cycle
Tool Request	Librarian	Capability description	Get tool recommendations
Execution Request	Fluxio	Action specification	Execute approved action
User Response	UI/API	Natural language + structured data	Deliver results
Nudge	User	Notification (priority, content, actions)	Proactive engagement

4.2 ODIE (Outcome Driven Intelligence Engine)

Inputs

Input	Source	Format	Purpose
Signal	Archer/Environment	Signal object	Trigger evaluation
Context	Cogniscient	Memory set with relevance scores	Ground reasoning
Action Hypotheses	Librarian	Action objects with expected deltas	Evaluation candidates
Execution Feedback	Fluxio	Result + observed signals	Learning input
Outcome Definitions	Configuration	Outcome objects (direction, measure, object)	Define goals

Outputs

Output	Destination	Format	Purpose
Active Outcome Pool	Cogniscient, Internal	Pool object (outcome IDs, weights)	Scope retrieval and evaluation
Scored Actions	Archer	Ranked action list with scores and reasoning	Inform selection
Satisfaction Updates	Outcome Store	Outcome ID + new satisfaction score	Track progress
Memory Reinforcement	Cogniscient	Memory ID + strength delta	Learning feedback
Experience Record	Cogniscient	Full context + action + outcome record	Store new memory

Key Data Objects

Outcome:

{
  "outcome_id": "OUT-001",
  "statement": "Minimize time to resolve customer support tickets",
  "direction": "minimize",
  "measure": "time_hours",
  "object": "support_ticket_resolution",
  "context": "customer_support_department",
  "importance": 0.85,
  "satisfaction": 0.45,
  "opportunity_score": 1.25,
  "parent_outcome_id": "OUT-000",
  "linked_signal_types": ["ticket_created", "ticket_resolved", "escalation"]
}

Action Hypothesis:

{
  "action_id": "ACT-001",
  "description": "Route high-priority tickets to senior agents",
  "tool_id": "TOOL-routing-001",
  "parameters": {"priority_threshold": "high", "agent_tier": "senior"},
  "expected_outcome_deltas": [
    {"outcome_id": "OUT-001", "expected_delta": 0.15, "confidence": 0.7},
    {"outcome_id": "OUT-002", "expected_delta": -0.05, "confidence": 0.6}
  ],
  "constraints_checked": ["CONST-001", "CONST-002"],
  "constraint_violations": []
}

4.3 Cogniscient (Adaptive Intelligence Substrate)

Inputs

Input	Source	Format	Purpose
Query	Archer/ODIE	Query object (text, filters, outcome pool)	Retrieve memories
Experience Record	ODIE	Experience object (full context)	Store new memory
Reinforcement Signal	ODIE	Memory ID + strength delta	Update memory strength
Entity Update	Connected Systems	Entity object	Update knowledge graph

Outputs

Output	Destination	Format	Purpose
Memory Set	Requester	Memories with relevance scores	Inform processing
Entity Context	Requester	Related entities and relationships	Ground understanding
Concept Assignments	Internal	Memory ID + concept IDs	Organize memory
Supersession Chain	Requester	Chronological belief/fact states	Track evolution

Key Data Objects

Memory:

{
  "memory_id": "MEM-001",
  "content": "Customer complained about 3-day wait for initial response",
  "embedding": [0.123, -0.456, ...],
  "strength": 0.75,
  "recency": 0.90,
  "reinforcement_count": 3,
  "formation_context": {
    "active_outcomes": ["OUT-001", "OUT-002"],
    "active_signals": ["ticket_escalation"],
    "timestamp": "2026-02-02T14:30:00Z"
  },
  "concept_ids": ["CONCEPT-customer-service", "CONCEPT-wait-time"],
  "emotional_markers": {"frustration": 0.8, "urgency": 0.6},
  "entity_links": ["ENTITY-customer-123", "ENTITY-ticket-456"]
}

Retrieval Request:

{
  "query_text": "customer wait time issues",
  "active_outcome_pool": [
    {"outcome_id": "OUT-001", "weight": 0.85},
    {"outcome_id": "OUT-002", "weight": 0.65}
  ],
  "retrieval_params": {
    "top_k": 10,
    "recency_weight": 0.3,
    "outcome_alignment_weight": 0.4,
    "semantic_weight": 0.3,
    "include_associative_leaps": true,
    "leap_depth": 2
  }
}

4.4 Librarian (Resource Manager)

Inputs

Input	Source	Format	Purpose
Capability Request	Archer/ODIE	Capability description	Find matching tools
Execution Feedback	Fluxio/ODIE	Tool ID + success/failure + context	Update scores
Tool Registration	Admin	Tool specification	Add to registry
Context	ODIE	Active outcomes, signal context	Contextual scoring

Outputs

Output	Destination	Format	Purpose
Tool Recommendations	Requester	Ranked tool list with scores	Guide selection
Action Hypotheses	ODIE	Action objects with expected deltas	Evaluation candidates
Tool Specification	Fluxio	Tool definition + parameters	Enable execution

Key Data Objects

Tool Registry Entry:

{
  "tool_id": "TOOL-query-builder-postgres",
  "name": "PostgreSQL Query Builder",
  "capabilities": ["sql_query", "database_read", "schema_introspection"],
  "base_score": 0.80,
  "contextual_scores": {
    "database_type:postgres": 0.95,
    "database_type:mysql": 0.60,
    "query_complexity:simple": 0.90,
    "query_complexity:complex": 0.70
  },
  "execution_history": {
    "total_invocations": 150,
    "success_rate": 0.87,
    "avg_duration_ms": 230
  },
  "parameters_schema": {
    "connection_string": "required",
    "query": "required",
    "timeout_ms": "optional, default: 5000"
  }
}

4.5 Fluxio (Execution Runtime)

Inputs

Input	Source	Format	Purpose
Execution Request	Archer	Action specification	Trigger execution
Tool Specification	Librarian	Tool definition	Enable invocation
Governance Rules	Policy Layer	Execution constraints	Enforce boundaries

Outputs

Output	Destination	Format	Purpose
Execution Result	Archer, ODIE	Result object (status, output, metrics)	Complete cycle
Execution Record	Cogniscient	Full execution context	Store experience
Tool Feedback	Librarian	Tool ID + performance metrics	Update scores
Observed Signals	ODIE	Signals detected during execution	Inform outcome updates

Key Data Objects

Execution Request:

{
  "execution_id": "EXEC-001",
  "action_id": "ACT-001",
  "tool_id": "TOOL-query-builder-postgres",
  "parameters": {
    "connection_string": "postgres://...",
    "query": "SELECT COUNT(*) FROM tickets WHERE status = 'open'"
  },
  "timeout_ms": 5000,
  "governance_context": {
    "user_id": "USER-001",
    "approval_required": false,
    "audit_level": "full"
  }
}

Execution Result:

{
  "execution_id": "EXEC-001",
  "status": "success",
  "output": {"count": 47},
  "metrics": {
    "duration_ms": 180,
    "tokens_used": 0,
    "cost_cents": 0
  },
  "observed_signals": [
    {"type": "query_executed", "content": {"table": "tickets", "operation": "SELECT"}}
  ],
  "timestamp": "2026-02-02T14:35:00Z"
}

Part 5: Continuous Learning Architecture

5.1 The Learning Loop

ARCHER implements continuous learning through three interconnected feedback mechanisms:

Memory Reinforcement (Cogniscient)

Memories that contribute to successful outcomes increase in strength
Memories that contribute to failures decrease in strength
Unused memories decay over time
Result: The system surfaces more useful information over time

Tool Scoring (Librarian)

Tools that succeed in context increase their contextual scores
Tools that fail decrease their scores
Scores are context-dependent (same tool scores differently in different situations)
Result: Better tool recommendations over time

Outcome Satisfaction (ODIE)

Successful actions update outcome satisfaction scores
Opportunity scores automatically recalculate
Focus shifts to remaining high-opportunity outcomes
Result: The system pursues the most impactful actions

5.2 Feedback Flow Diagram

                    ┌─────────────────────────────────────────┐
                    │           DECISION CYCLE                │
                    │  Signal → Pool → Score → Filter → Act   │
                    └────────────────────┬────────────────────┘
                                         │
                                         ▼
                    ┌─────────────────────────────────────────┐
                    │              EXECUTION                   │
                    │         Fluxio invokes tool             │
                    └────────────────────┬────────────────────┘
                                         │
                                         ▼
                    ┌─────────────────────────────────────────┐
                    │              OBSERVATION                 │
                    │      Capture result, detect signals     │
                    └────────────────────┬────────────────────┘
                                         │
                    ┌────────────────────┼────────────────────┐
                    │                    │                    │
                    ▼                    ▼                    ▼
          ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
          │ MEMORY UPDATE   │ │  TOOL UPDATE    │ │ OUTCOME UPDATE  │
          │ (Cogniscient)   │ │  (Librarian)    │ │ (ODIE)          │
          │                 │ │                 │ │                 │
          │ Reinforce or    │ │ Adjust context  │ │ Update          │
          │ weaken memories │ │ scores          │ │ satisfaction    │
          │ that influenced │ │                 │ │ Recalculate     │
          │ this decision   │ │                 │ │ opportunity     │
          └────────┬────────┘ └────────┬────────┘ └────────┬────────┘
                   │                   │                   │
                   └───────────────────┴───────────────────┘
                                       │
                                       ▼
                    ┌─────────────────────────────────────────┐
                    │         NEXT DECISION CYCLE             │
                    │   (Informed by updated state)           │
                    └─────────────────────────────────────────┘

5.3 What Makes This Different from Traditional "Learning"

Traditional Approach	ARCHER Continuous Learning
Periodic retraining	Every action produces feedback
Batch updates	Real-time updates
Model weights change	Memory strength, tool scores, satisfaction change
Requires labeled data	Self-supervised through outcome comparison
Expensive compute	Lightweight score adjustments
Risk of catastrophic forgetting	Graceful decay with supersession tracking

Appendix A: Use Case Test Matrix

Use Case	ODIE	Cogniscient	Librarian	Fluxio	Archer
UC1: SQL Query	Weights accuracy vs speed outcomes	Recalls schema, past queries	Selects query builder	Executes query	Orchestrates, presents
UC2: Tool Selection	Provides outcome context	Provides historical performance	Scores and ranks tools	Validates executability	Presents recommendation
UC3: Memory Decay	Filters by active outcomes	Applies temporal decay	—	—	Ensures relevance
UC4: Conceptual Search	Activates related outcomes	Traverses concept graph	—	—	Synthesizes response
UC5: Continuous Learning	Compares expected vs actual	Updates memory strength	Updates tool scores	Reports metrics	Logs experience
UC6: Cross-Client Isolation	Scopes outcomes by tenant	Enforces tenant boundaries	—	Respects permissions	Enforces governance
UC7: Transcript Processing	Tags with active outcomes	Extracts concepts, stores	—	—	Presents results
UC8: NPC Motivation	Evaluates action hypotheses	Recalls relationship history	—	Executes behavior	Maintains narrative
UC9: Support Optimization	Detects outcome regression	Recalls resolution patterns	Recommends escalation	Triggers notification	Coordinates response
UC10: Belief Revision	Triggers revision, propagates	Updates confidence, genealogy	—	—	Surfaces change

Appendix B: Differentiation Summary Table

Capability	Traditional RL	Offline LLM	RAG	Graph RAG	ARCHER
Explicit Goals	✗	✗	✗	✗	✓
Multi-Objective	Partial	✗	✗	✗	✓
Persistent Memory	Replay buffer	✗	✗	✓	✓
Outcome-Weighted Retrieval	✗	✗	✗	✗	✓
Continuous Learning	Requires training	✗	✗	✗	✓
Experience Storage	✗	✗	Text chunks	Entities	Full context
Emotional Dimension	✗	✗	✗	✗	✓
Belief Revision	✗	✗	✗	Partial	✓
Explainable Decisions	✗	Partial	Partial	Partial	✓
Human-Defined Outcomes	✗	✗	✗	✗	✓

Document generated February 2026. For implementation guidance, see ARCHER_Suite_Implementation_Guide_for_Claude_Code.md