sequenceDiagram participant Client as External Client participant Registry as Agent Registry participant Discovery as Discovery Service participant AgentA as Agent A (Coder) participant AgentB as Agent B (Reviewer) participant AgentC as Agent C (Optimizer) participant Coordinator as MABEAM Coordinator Note over Client,Coordinator: T0: System Bootstrap & Discovery Registry->>+Discovery: start_discovery_scan() par Agent Capability Broadcasting AgentA->>Registry: register_capabilities([code_generation, python, elixir]) AgentB->>Registry: register_capabilities([code_review, quality_analysis, security]) AgentC->>Registry: register_capabilities([optimization, simba, hyperparameter_tuning]) and Discovery Process Discovery->>+Registry: scan_registered_agents() Registry-->>Discovery: agent_list[{id, capabilities, load, metadata}] Discovery->>Discovery: build_capability_index() Discovery->>Discovery: calculate_compatibility_matrix() end Note over Client,Coordinator: T1: Task Request Arrives (Complex ML Pipeline) Client->>+Coordinator: request_task_execution({ task: "implement_ml_pipeline", requirements: [code_generation, code_review, optimization], priority: high, deadline: 300_seconds }) Note over Client,Coordinator: T2: Capability Matching & Team Assembly Coordinator->>+Discovery: find_optimal_team(requirements: [code_generation, code_review, optimization]) par Capability Analysis Discovery->>Discovery: capability_match_score(AgentA, code_generation) -> 0.95 Discovery->>Discovery: capability_match_score(AgentB, code_review) -> 0.88 Discovery->>Discovery: capability_match_score(AgentC, optimization) -> 0.92 and Load Balancing Check Discovery->>Registry: get_agent_load(AgentA) -> {queue: 2, active: 1, cpu: 45%} Discovery->>Registry: get_agent_load(AgentB) -> {queue: 0, active: 0, cpu: 12%} Discovery->>Registry: get_agent_load(AgentC) -> {queue: 1, active: 1, cpu: 78%} and Compatibility Matrix Discovery->>Discovery: check_agent_compatibility(AgentA, AgentB) -> 0.91 Discovery->>Discovery: check_agent_compatibility(AgentB, AgentC) -> 0.89 Discovery->>Discovery: check_agent_compatibility(AgentA, AgentC) -> 0.94 end Discovery->>Discovery: calculate_team_score({A,B,C}) -> 0.887 (weighted average) Discovery-->>Coordinator: optimal_team[{agent_a: 0.95, agent_b: 0.88, agent_c: 0.92}, team_score: 0.887] Note over Client,Coordinator: T3: Team Coordination Setup Coordinator->>+AgentA: establish_coordination_channel(session_id: #ref456, team: [B,C]) Coordinator->>+AgentB: establish_coordination_channel(session_id: #ref456, team: [A,C]) Coordinator->>+AgentC: establish_coordination_channel(session_id: #ref456, team: [A,B]) par Coordination Channels AgentA-->>Coordinator: channel_ready(session_id: #ref456) AgentB-->>Coordinator: channel_ready(session_id: #ref456) AgentC-->>Coordinator: channel_ready(session_id: #ref456) and Task Distribution Coordinator->>AgentA: assign_task({phase: code_generation, inputs: requirements, dependencies: []}) Coordinator->>AgentB: assign_task({phase: code_review, inputs: [], dependencies: [AgentA]}) Coordinator->>AgentC: assign_task({phase: optimization, inputs: [], dependencies: [AgentA, AgentB]}) end Note over Client,Coordinator: T4: Coordinated Execution Pipeline AgentA->>AgentA: execute_code_generation() -> generated_code AgentA->>+Coordinator: task_complete(phase: code_generation, output: generated_code, next: [AgentB]) Coordinator->>+AgentB: dependency_satisfied(input: generated_code, from: AgentA) AgentB->>AgentB: execute_code_review(generated_code) -> review_report AgentB->>+Coordinator: task_complete(phase: code_review, output: review_report, next: [AgentC]) Coordinator->>+AgentC: dependency_satisfied(input: {generated_code, review_report}, from: [AgentA, AgentB]) AgentC->>AgentC: execute_optimization(generated_code, review_report) -> optimized_solution AgentC->>+Coordinator: task_complete(phase: optimization, output: optimized_solution, next: []) Coordinator->>Coordinator: aggregate_results([generated_code, review_report, optimized_solution]) Coordinator-->>Client: task_execution_complete(result: optimized_solution, metadata: execution_stats) Note over Client,Coordinator: Total Execution Time: 127ms (85ms work + 42ms coordination)

graph TB subgraph "T0: Task Announcement (Broadcast Phase)" TaskCoord[Task Coordinator
auction_id: #ref789] Task[High-Value Task
reward: 100 credits
complexity: 0.8
deadline: 60s] TaskCoord -.->|broadcast_auction| Agent1[Agent 1
Specialist: NLP
Load: 20%
Credits: 450] TaskCoord -.->|broadcast_auction| Agent2[Agent 2
Specialist: Vision
Load: 60%
Credits: 230] TaskCoord -.->|broadcast_auction| Agent3[Agent 3
Specialist: General
Load: 30%
Credits: 680] TaskCoord -.->|broadcast_auction| Agent4[Agent 4
Specialist: NLP
Load: 80%
Credits: 120] TaskCoord -.->|broadcast_auction| Agent5[Agent 5
Specialist: Optimization
Load: 10%
Credits: 890] end subgraph "T1: Bid Calculation (Parallel Decision Making)" Agent1 -.->|evaluate| Calc1{Bid Calculator
Agent 1} Agent2 -.->|evaluate| Calc2{Bid Calculator
Agent 2} Agent3 -.->|evaluate| Calc3{Bid Calculator
Agent 3} Agent4 -.->|evaluate| Calc4{Bid Calculator
Agent 4} Agent5 -.->|evaluate| Calc5{Bid Calculator
Agent 5} Calc1 -.->|factors| Factors1[Capability Match: 0.9
Load Factor: 0.8
Reward Appeal: 0.7
Risk Assessment: 0.3
🎯 BID: 85 credits] Calc2 -.->|factors| Factors2[Capability Match: 0.4
Load Factor: 0.4
Reward Appeal: 0.9
Risk Assessment: 0.7
❌ NO BID (low capability)] Calc3 -.->|factors| Factors3[Capability Match: 0.7
Load Factor: 0.7
Reward Appeal: 0.6
Risk Assessment: 0.4
🎯 BID: 75 credits] Calc4 -.->|factors| Factors4[Capability Match: 0.9
Load Factor: 0.2
Reward Appeal: 1.0
Risk Assessment: 0.8
❌ NO BID (overloaded)] Calc5 -.->|factors| Factors5[Capability Match: 0.6
Load Factor: 0.9
Reward Appeal: 0.5
Risk Assessment: 0.2
🎯 BID: 65 credits] end subgraph "T2: Bid Collection & Evaluation (15ms window)" Factors1 -.->|submit_bid| BidQueue[Bid Collection Queue
Timeout: 15ms] Factors3 -.->|submit_bid| BidQueue Factors5 -.->|submit_bid| BidQueue BidQueue -.->|sort_by_value| BidRank{Bid Ranking
1st: Agent1 (85 credits)
2nd: Agent3 (75 credits)
3rd: Agent5 (65 credits)} BidRank -.->|winner_selection| WinnerLogic[Winner Selection Logic
Criteria: Highest Bid + Risk Assessment
Winner: Agent1 (85 credits, risk: 0.3)] end subgraph "T3: Auction Resolution & Contract" WinnerLogic -.->|award_contract| Contract[Contract Execution
Agent1 ← Task
Reward: 85 credits
Penalty: 25 credits if failed] Contract -.->|notify_losers| Notify1[Agent3: lost_auction] Contract -.->|notify_losers| Notify2[Agent5: lost_auction] Contract -.->|notify_non_bidders| Notify3[Agent2: auction_ended] Contract -.->|notify_non_bidders| Notify4[Agent4: auction_ended] Contract -.->|start_execution| Execute[Agent1 Task Execution
Status: IN_PROGRESS
Progress Monitoring: ON
Deadline Tracking: 60s] end subgraph "T4: Execution Monitoring & Settlement" Execute -.->|progress_updates| Monitor[Progress Monitor
25% complete: 15s
50% complete: 28s
75% complete: 41s
100% complete: 53s] Monitor -.->|success| Settlement[Settlement System
Task: COMPLETED
Quality Score: 0.92
Time: 53s (under deadline)
💰 Award: 85 credits → Agent1
📊 Update Reputation: +0.05] Settlement -.->|update_economics| Economics[Economic System Update
Agent1: 450 + 85 = 535 credits
Agent1 Success Rate: 94.2% → 94.4%
Task Pool: -1 task
Market Liquidity: High] end classDef highbid fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px classDef mediumbid fill:#fff3e0,stroke:#ef6c00,stroke-width:2px classDef lowbid fill:#e3f2fd,stroke:#1565c0,stroke-width:1px classDef nobid fill:#ffebee,stroke:#c62828,stroke-dasharray: 5 5 classDef winner fill:#e8f5e8,stroke:#388e3c,stroke-width:4px classDef monitor fill:#f3e5f5,stroke:#7b1fa2 class Factors1,Agent1 highbid class Factors3,Agent3 mediumbid class Factors5,Agent5 lowbid class Factors2,Factors4,Agent2,Agent4 nobid class Contract,Execute,WinnerLogic winner class Monitor,Settlement,Economics monitor

flowchart TD subgraph "Decision Context: ML Model Selection" DecisionReq[Decision Required
Select ML Model for Production
Stakeholder Agents: 7
Consensus Threshold: 60%
Options: [BERT, GPT-4, Claude, Custom]] end subgraph "T0-T5s: Information Gathering Phase" Agent_Data[Data Scientist Agent
Preference: Custom Model
Reasoning: Domain-specific needs] Agent_Perf[Performance Agent
Preference: BERT
Reasoning: Latency requirements] Agent_Cost[Cost Agent
Preference: BERT
Reasoning: Budget constraints] Agent_Quality[Quality Agent
Preference: GPT-4
Reasoning: Accuracy metrics] Agent_Ops[Operations Agent
Preference: BERT
Reasoning: Deployment simplicity] Agent_Security[Security Agent
Preference: Custom
Reasoning: Data privacy] Agent_Product[Product Agent
Preference: Claude
Reasoning: Feature completeness] DecisionReq -.->|gather_input| Agent_Data DecisionReq -.->|gather_input| Agent_Perf DecisionReq -.->|gather_input| Agent_Cost DecisionReq -.->|gather_input| Agent_Quality DecisionReq -.->|gather_input| Agent_Ops DecisionReq -.->|gather_input| Agent_Security DecisionReq -.->|gather_input| Agent_Product end subgraph "T5-T10s: Initial Voting Round" VoteRound1{Initial Vote Tally
BERT: 3 votes (43%)
GPT-4: 1 vote (14%)
Claude: 1 vote (14%)
Custom: 2 votes (29%)
❌ No Consensus (need 60%)} Agent_Data -.->|vote: Custom| VoteRound1 Agent_Perf -.->|vote: BERT| VoteRound1 Agent_Cost -.->|vote: BERT| VoteRound1 Agent_Quality -.->|vote: GPT-4| VoteRound1 Agent_Ops -.->|vote: BERT| VoteRound1 Agent_Security -.->|vote: Custom| VoteRound1 Agent_Product -.->|vote: Claude| VoteRound1 end subgraph "T10-T15s: Negotiation & Preference Exchange" NegotiationFacilitator[Negotiation Facilitator
Identify Compromise Opportunities
Analyze Preference Similarities
Suggest Trade-offs] VoteRound1 -.->|trigger_negotiation| NegotiationFacilitator NegotiationFacilitator -.->|analysis| PrefMatrix[Preference Analysis
Cost + Performance align on BERT
Data + Security align on Custom
Quality stands alone on GPT-4
Product stands alone on Claude
💡 BERT has strongest coalition] PrefMatrix -.->|facilitate_discussion| Discussion[Agent Discussion Round
Quality Agent: "What if BERT + fine-tuning?"
Security Agent: "Custom allows better control"
Cost Agent: "BERT lowest TCO"
Product Agent: "Claude has better APIs"] end subgraph "T15-T20s: Informed Re-voting" Discussion -.->|trigger_revote| VoteRound2{Second Vote Tally
BERT + Fine-tuning: 5 votes (71%)
Custom: 1 vote (14%)
Claude: 1 vote (14%)
✅ CONSENSUS ACHIEVED (71% > 60%)} Agent_Data -.->|changed_vote: BERT+FT| VoteRound2 Agent_Perf -.->|vote: BERT+FT| VoteRound2 Agent_Cost -.->|vote: BERT+FT| VoteRound2 Agent_Quality -.->|changed_vote: BERT+FT| VoteRound2 Agent_Ops -.->|changed_vote: BERT+FT| VoteRound2 Agent_Security -.->|vote: Custom| VoteRound2 Agent_Product -.->|vote: Claude| VoteRound2 end subgraph "T20-T25s: Decision Implementation" VoteRound2 -.->|implement_decision| Implementation[Decision Implementation
Selected: BERT + Fine-tuning
Consensus Level: 71%
Dissenting Agents: 2
Implementation Priority: HIGH] Implementation -.->|notify_all| NotifyAll[Notification Broadcast
Decision: BERT + Fine-tuning selected
Rationale: Cost-performance balance with customization
Timeline: Implementation begins immediately
Dissent Recorded: Security & Product concerns noted] Implementation -.->|track_outcome| OutcomeTracking[Outcome Tracking Setup
Success Metrics: [latency, accuracy, cost, security]
Review Period: 30 days
Dissent Review: If metrics fail, revisit Custom/Claude options] end classDef bert fill:#4caf50,stroke:#2e7d32,stroke-width:3px classDef custom fill:#ff9800,stroke:#ef6c00,stroke-width:2px classDef gpt4 fill:#2196f3,stroke:#1565c0,stroke-width:2px classDef claude fill:#9c27b0,stroke:#7b1fa2,stroke-width:2px classDef consensus fill:#66bb6a,stroke:#2e7d32,stroke-width:4px classDef negotiation fill:#ffc107,stroke:#f57c00,stroke-width:2px class Agent_Perf,Agent_Cost,Agent_Ops,VoteRound2 bert class Agent_Data,Agent_Security custom class Agent_Quality gpt4 class Agent_Product claude class Implementation,NotifyAll,OutcomeTracking consensus class NegotiationFacilitator,PrefMatrix,Discussion negotiation

gantt title Dynamic Resource Allocation - Live System Response dateFormat X axisFormat %L section System_Load_Monitor Baseline Monitoring :monitor1, 0, 50ms Load Spike Detection :milestone, spike1, 50, 0ms Resource Analysis :analysis1, after spike1, 25ms Allocation Decision :decision1, after analysis1, 15ms section Agent_Pool_A Normal Load (30%) :normal1, 0, 50ms Spike Handling (95%) :spike_a, after normal1, 40ms Overload Alert :milestone, alert1, after spike_a, 0ms Request Redirect :redirect1, after alert1, 10ms section Agent_Pool_B Low Load (15%) :low1, 0, 50ms Incoming Redirects :incoming1, after low1, 30ms Load Increase (65%) :increase1, after incoming1, 25ms Stabilization :stable1, after increase1, 15ms section Agent_Pool_C Medium Load (45%) :med1, 0, 50ms Partial Redirects :partial1, after med1, 35ms Load Increase (70%) :increase2, after partial1, 20ms Peak Performance :peak1, after increase2, 10ms section New_Agent_Spawn Spawn Decision :milestone, spawn_decision, 90, 0ms Agent Initialization :init1, after spawn_decision, 25ms Registration :reg1, after init1, 10ms Load Assignment :assign1, after reg1, 5ms section Load_Rebalancing Rebalance Trigger :milestone, rebalance, 130, 0ms Calculate Optimal :calc1, after rebalance, 20ms Migrate Tasks :migrate1, after calc1, 15ms System Stabilized :milestone, stable_final, after migrate1, 0ms