Pathways & Connecting the dots to Collective AGI

Dynamic Specialization and Synergy

Agents form, dissolve, and reform task oriented coalitions that express division of labor and complementary skills.

Coalition triggers: a task spec, budget, and policy context arrive at the task layer. The routing layer scores candidates by capability fit, locality to data and tools, price, load, and policy compliance.
Division of labor: planners, workers, critics, judges, explorers, curators, stewards. Roles are explicit in the workflow. Each subtask carries typed I O and tests.
Assembly primitives: typed interfaces, contract bindings, and reusable subplans from the plan library. Composers stitch capabilities into plans.
Adaptive execution: if a step fails, local repair swaps a worker or adjusts constraints without halting the plan. Critics propose fixes. Judges verify against tests.
Micro economies: coalitions operate with local budgets and SLAs. Pricing, bidding, or contract net patterns allocate scarce resources.
Social signals: reputation, endorsements, and past co work patterns increase the chance that the right teams self assemble quickly.
Outcome capture: artifacts, traces, and performance metrics persist in the knowledge mesh for future reuse.
Key metrics: plan quality per cost, cycle time, parallelism ratio, repair rate, coalition stability, knowledge reuse rate.

Semantic Interoperability and Composability

Diversity becomes capability only if parts fit together. AgentGrid makes that fit reliable.

Typed interfaces and schemas: All tasks and artifacts carry strict types. Contract tests prevent silent breakage.
Ontologies and translators: Shared concepts with adapters for model or tool dialects. Versioning handles drift.
Reusable subplans: Proven decompositions become plan templates. Composers assemble larger capabilities from these blocks.
Compatibility matrix: Tracks which tools and agents interoperate. Curators retire brittle combos.
Failure isolation: Schema mismatches fail fast at boundaries. Rollback and retry are local.
Key metrics: Composition success rate, schema drift incidents, plan reuse ratio, rollback frequency, time to assemble a new plan.

Distributed Learning

The grid learns at multiple scales by sharing knowledge and credit across agents and plans.

Knowledge mesh: stores artifacts, trajectories, embeddings, prompts, tool adapters, and audits. Retrieval supports planning and execution.
Horizontal learning: peer to peer transfer of prompts, tool bindings, search strategies, and heuristics. Similar agents copy useful patterns through imitation or distillation.
Meta learning: selectors update routing policies. composers update planning priors. critics and judges refine evaluation rubrics. Bandits or RL use reward signals from task outcomes.
Multi scale loops:
Micro loop per agent updates local policies and tool use.
Meso loop per plan updates decomposition patterns and role assignments.
Macro loop updates market parameters, governance checks, and plan library promotion rules.
Credit assignment: causal logs attribute contributions to roles and steps. Rewards, reputation, and slashing signals shape future selection.
Safety learning: failed or unsafe behaviors become negative examples. PolicyGrid updates allow lists, constraints, and tests. Counterfactual probes harden plans before release.
Transfer and generalization: success in one domain seeds skills in adjacent domains through shared subplans and tool adapters. Cold start times shrink as the library grows.
Key metrics: transfer gain, planning reuse rate, routing regret, defect escape rate, time to safe competence in a new domain.

Resilience through Diversity

A diverse population of agents, models, and strategies makes the system robust and creative.

Diversity types: neural, symbolic, hybrid, evolutionary, program synthesis; varied search strategies and risk profiles; different tools and data access; varied policy stacks for jurisdictions and orgs.
Redundancy and ensembles: multiple agents try alternative plans. Voting, adjudication, or confidence weighted merges produce stable results.
Failure isolation: sharded cognition confines faults to local shards. Circuit breakers, timeouts, and backpressure prevent cascading failures.
Degraded modes: if a top expert is unavailable, the router picks a fallback plan with lower cost or lower risk. Graceful degradation maintains progress.
Adversarial robustness: reputation systems, staking, and audits reduce the impact of malicious or low quality agents. Byzantine tolerant checks catch outliers.
Economic robustness: price and budget diversity spreads load across providers. No single vendor or model becomes a choke point.
Exploration versus exploitation: swarms of small planners explore new options while stable pipelines exploit known good paths. The balance shifts with task difficulty and risk.
Key metrics: performance under targeted removals, variance under load spikes, adversarial detection rate, ensemble uplift over best single agent, recovery time from faults.

Sharded Cognition and Swarm Planning

Planning and reasoning are spread across many small planners and shards.

Shard sizing: Subtasks expert intelligence requirements, sized by data gravity, tool constraints, and skill depth.
Parallel search: Many planners explore alternative decompositions at once.
Anytime behavior: Early feasible plans deliver partial value while better plans converge.
Search control: Bandits prune weak branches. Early stopping reduces waste.
Merge and verify: Shard outputs reassemble with tests, audits, and causal logs.
Key metrics: Time to first feasible plan. Optimality gap at deadline. Search width and depth. Shard throughput. Merge defect rate.

Routing and Expert Matching

The router operationalizes division of labor by sending each subtask to the right expert.

Scoring features: Capability fit, reputation vectors, policy compliance, price, queue depth, locality to data and tools.
Learning policy: Contextual bandits or RL tune routing from outcomes.
Fallback logic: Tiered experts, safe defaults, and cached results maintain progress under churn.
Placement near data: Moves work to data and tools, not the other way around.
Key metrics: Routing regret. Expert hit rate. Queue wait time. Task latency. Failure on first placement vs after one swap.

Coalition Formation and Division of Labor

Teams form for a purpose, execute, and dissolve without central scheduling.

Triggers: A task with spec, tests, budget, and policy context arrives.
Roles: Planners, workers, critics, judges, explorers, curators, stewards.
Allocation: Auctions, contract net, or budget based contracts assign roles.
SLAs and escrow: Delivery, quality, and payment are bound to tests and audits.
Dissolution: Team disbands. Artifacts persist in the mesh for reuse.
Key metrics: Coalition spin up time. Role fill rate. SLA adherence. Cost per accepted artifact. Reuse of coalition outputs.

Social structure improves discovery, trust, and multi stakeholder choices.

Relationship graph: Endorsements, co work history, and team fit scores.
Team recall: Known good coalitions can be reformed quickly.
Proposals and votes: Plans with tradeoffs go to lightweight deliberation.
Preference aggregation: Multi objective choices balance cost, risk, fairness, and performance.
Key metrics: Decision latency. Participation rate. Endorsement accuracy. Post decision regret. Team reform success.

Communication Mesh

Shared context for intent, roles, decisions, results, and proofs across orgs and geographies. Keeps parallel teams aligned so many partial views converge into one outcome.

Carries requests, offers, context, approvals, commitments, exceptions etc. Plans move forward through clear act types that everyone can interpret.
Common operating picture: Allows plan goals, constraints, seeking, handoffs, and acceptances to be communicated to all relevant agents for coherent understanding.
Compression of coordination cost: The mesh reduces pairwise negotiation. Teams publish what others need to know, when they need to know it. Coordination scales with work, not with the number of parties.

Turns stakeholder preferences into executable directives before code runs, giving legitimacy and clear constraints to large plans.

Proposals and tradeoffs: scope, risk, budget, and policy options are compared with transparent rationale.
Choice mechanisms: approval or score voting for speed, ranked methods when tradeoffs are tight.
Constraint binding: winning outcomes compile into directives that guide routing, staffing, and contracts.
Delegation and proxies: reduces decision fatigue while preserving accountability.
Key metrics: decision latency, participation rate, welfare gain over baseline, post-decision regret, policy-conflict rate in execution.

Agency and Organization

Agents act for themselves or for institutions with clear authority and guardrails.

Identity and delegation. Keys and attestations define who may act and on what.
Budgets and policies. Wallets and policy stacks constrain spend and behavior.
Nested orgs. Coordinators represent teams while preserving member accountability.
Audit trails. Every action links to authority, policy, and budget.
Key metrics. Policy violation rate. Unauthorized action rate. Budget utilization. Audit pass rate. Time to resolve disputes.

AI Marketplace

Aligns supply with demand for agents, tools, and plan templates so capability gaps are discovered and filled.

Rich listings: capability, safety claims, SLOs, tests, prices, and compatibility.
Discovery signals: embeddings, tags, reputation, endorsements, and policy fit.
Curation and certification: promotes reliable assets, deprecates brittle ones.
Market depth: alternatives for critical roles reduce lock-in and raise resilience.
Key metrics: match rate, time to fill, listing reliability score, price dispersion for common roles, time to close identified capability gaps.

Task Exchange

Implements division of labor at market speed by matching typed tasks to specialists and coalitions.

Posting and context: spec, tests, budget, and policy travel with each task.
Bidding and negotiation: single roles or full teams staffed to solve tasks via auctions or contract-net patterns.
Milestones and escrow: Stage wise progress, acceptances release rewards and reduce risk.
Re-auction and fallback: failed steps are reassigned without stalling the plan.
Key metrics: time to first bid, acceptance rate, re-auction rate, cost per accepted artifact, coalition staffing success.

Contract System

Makes plans into accountable promises with machine-readable obligations, tests, and remedies.

Typed SLAs: roles, deliverables, SLOs, budgets, and policies expressed as executable terms.
Enforcement hooks: preflight checks, runtime guards, and post-run audits tied to contract clauses.
Dispute flow: evidence from traces and audits resolves disagreements quickly.
Versioning: safe amendments for long-running programs without breaking work.
Key metrics: breach rate, auto-resolution rate, mean time to resolution, audit pass rate, on-time milestone completion.

Knowledge Mesh and Memory

Collective memory is how the grid compounds its wins.

Unified artifact store. Prompts, traces, embeddings, models, adapters, audits, and reports.
Retrieval for planning. Past plans, subplans, and hints are pulled into new work.
Summarization and lineage. Condense long traces. Preserve provenance.
De duplication. Curators merge near duplicates to reduce noise.
Key metrics. Retrieval hit rate. Time saved by reuse. Stale artifact rate. Duplicate ratio. Provenance completeness.

Safety and Polycentric Governance

Alignment is enforced where work happens, not after the fact.

Policy stacks. Domain, jurisdiction, and organization specific constraints.
Preflight checks. Plans are probed with counterfactual tests before execution.
Runtime enforcement. Monitors, circuit breakers, and sandbox fallbacks.
Postmortems. Incidents produce rules, tests, and examples for future prevention.
Key metrics. Policy breach rate. Mean time to detect. Mean time to repair. Audit coverage. Residual risk score.

Comprehensive Growth Dynamics

Scale increases diversity. Diversity increases coverage and quality.

Capability graph densification. More nodes and edges create more solution paths.
Frontier expansion. Explorers add skills. Curators standardize them.
Diminishing returns detection. Metrics inform where to invest in new skills vs more capacity.
Key metrics. Domain coverage curve. Solution path count per task class. Marginal quality gain per added agent. Cost per new capability.