Executive Summary

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MiRuntime’s existing framework defines a layered Machine Intelligence Runtime (MIR) that mediates between models and applications. Its seven-layer reference architecture (from Application down to Evidence & Telemetry) and developer guide emphasize explicit contracts, observability, and controlled side effects. Core components (e.g. ModelAdapters, ToolRegistry, PermissionPolicy, EvidenceLedger etc.) form a “minimum viable runtime surface”. However, MIR’s current design assumes externally provided goals and policies, with no built-in model of self-maintenance or evolving objectives.

We surveyed state-of-the-art research on AI runtime infrastructure and agentic systems. A recent formalism ("AI Runtime Infrastructure") argues for an active execution-time layer above the model that observes, intervenes, and optimizes safety and efficiency during an agent’s workflow. Independent literature on agentic AI highlights recursive perception–action loops with inner self-monitoring loops that create a “synthetic teleology” (self-regulating purposiveness). Teleodynamic theory (autopoiesis, homeostasis) adds that any new structure must pay its maintenance cost, that “no-op” decisions preserve resource balance, and that systems should form internal goal hierarchies.

Teleodynamic integration: We propose concrete MIR extensions to capture these concepts. For example, a Goal Hierarchy API and data model would let the runtime track sub-goals and meta-goals (echoing hierarchical RL). A Reflective Critic component could monitor execution traces against success criteria, triggering internal goal revisions or rollbacks. An Intrinsic Value function in the runtime contract could encode homeostatic objectives (e.g. penalizing resource overuse), biasing the agent toward no-op when changes aren’t justified. These maps to new APIs/endpoints (e.g. GET /runtime/goals, POST /runtime/state/reflect) and UX elements for inspecting the agent’s internal goals and viability metrics.

We outline a phased roadmap: Phase 1 (design & proof-of-concept: schema for goals, simple monitors; Effort = Medium; Risks = concept complexity) with metrics like goal-coherence and overhead; Phase 2 (implement self-regulation loops, policy integration; Effort = High; Risks = integration and debugging complexity); Phase 3 (refinement and alignment: adaptive budgets, formal verification; Effort = High). Each phase includes milestones, example APIs, and safety checks.

Security/Safety: We build on MIR’s governance (e.g. pre/post approvals, typed schemas) and emerging standards. OWASP emphasizes guarding against goal manipulation and unintended self-modification by audit trails and validation; we incorporate these by cryptographic logging of goal edits and consistency checks on any agent-driven objective changes. Alignment is enforced by embedding normative constraints in the runtime policy layer (as multi-stakeholder preferences) and limiting self-directed adjustments to approved domains.

Finally, we compare three architecture styles (Monolithic, Microservices/Agents, Hybrid) across scalability, latency, developer ergonomics, and teleodynamic support in a summary table. (Monoliths are simplest but scale less; microservices can distribute teleodynamic loops at cost of complexity; hybrid strikes a balance.) We also include mermaid diagrams illustrating the enhanced runtime flows and components.

This report synthesizes MiRuntime’s vision, recent research on AI execution environments, and teleological systems theory. Citations are included for all claims and design choices.

1. MiRuntime Today: Architecture, APIs, Goals, and Gaps

MiRuntime (MIR) positions itself as “the runtime layer after the model”. Its reference architecture comprises seven layers: Application (front-end & triggers), Runtime Orchestration (sessions, loops, retries), Policy/Permissions, Memory & Context, Tools/Connectors, Model Adapters, and Evidence/Telemetry. The design principles emphasize replaceable models, explicit tool contracts, scoped context, and reviewable side effects. For example, model-specific details are isolated in adapters, and every tool has a typed input/output schema, permission class, and failure mode. A key principle is observability: “Machine intelligence should remain observable, constrained, recoverable, and reviewable while it runs”.

The Developer Guide reiterates these aims: it urges building adapters to avoid model lock-in, designing tools as contracts with explicit schemas, separating planning vs. side-effect execution, and exposing state (context, memory, traces) to developers. Its “minimum viable runtime surface” lists components like ModelAdapters, ToolRegistry, PermissionPolicy, MemoryPackages, RetrievalConnectors, TaskCheckpoints, EvidenceLedger, ErrorRecovery, etc. (see diagram). The runtime contract is illustrated by a JSON schema snippet: it includes fields for intent (objective, risk class, success criteria), context (sources, memory scope), authority (allowed tools, write policy, required approvals), execution (state, checkpoint policy, budgets), and evidence (events, artifacts, unresolved uncertainty). MIR’s implementation checklist further mandates classifying actions (read-only, reversible, destructive), requiring human review for irreversible actions, logging all traces, and scoping memory by permission.

Despite this robust foundation, gaps remain. MiRuntime’s scope focuses on ensuring dependable execution of externally specified tasks, but it does not yet encompass any intrinsic purposiveness. There is no built-in mechanism for the runtime to generate or revise its own goals: all objectives and success criteria come from outside the runtime (e.g. user intent). Teleodynamic ideas like self-maintenance, homeostasis, or emergent goal hierarchies are not mentioned. For instance, MIR lacks any notion of an agent’s internal value function or bias towards no-op when unable to justify changes. In effect, the runtime enforces external policies but does not itself hold goals or a cost function beyond resource budgets. We therefore identify integration of self-directed goal management and adaptive stability controls as a key extension area.

<!-- MERMAID DIAGRAM: Reference Flow -->

flowchart TD
    subgraph MIR_Flow
      A(Intent Capture) --> B(Context Assembly)
      B --> C(Model Planning)
      C --> D(Runtime Validation)
      D --> E(Tool Execution)
      E --> F(Evidence Capture)
      F --> G(User Handoff)
    end
    class A,B,C,D,E,F,G box;

2. Machine Intelligence Runtimes & Agent Platforms

Modern AI agent runtimes take MIR’s prescription further by actively managing execution. Recent research formalizes an “AI Runtime Infrastructure” layer: an active execution-time system between the model and the application, continuously observing agent state and intervening to improve success, latency, and safety. Cruz et al. (2026) describe this layer as treating execution itself as an optimization surface: it performs adaptive memory management, failure detection and recovery, and policy enforcement “over long-horizon agent workflows”. In their view, existing stacks (model serving, orchestration frameworks, post-hoc observability) do not address emergent errors during run-time. Formal requirements for runtime infra include 1) continuous visibility into multi-step execution, 2) active intervention capabilities (e.g. adjusting control flow or triggering recovery), and 3) long-horizon state awareness.

Industry platforms echo these trends. For example, LangChain’s recent analysis stresses that production agent frameworks must expose durable state and internal logs, not obscure them under high-level abstractions. They argue that observability and reliability distinguish demo systems from production: durable execution, reliable state persistence, and clear failure modes are essential once agents handle real workloads. Agent frameworks like LangSmith, CrewAI, Microsoft’s Agent Framework, etc. offer primitives for tool-calling, memory management and inter-agent messaging, with an emphasis on debugging, evaluation and guardrails.

Cloud providers provide managed orchestrators: e.g. AWS’s AgentCore Runtime is a serverless agent host integrated with API gateways, IAM, and scaling services. AWS advises starting with simple architectures (e.g. API Gateway + single runtime) and only adding orchestration, queueing, and workflow services as scale demands. In practice, architectures range from monolithic agents to distributed microservices. The AWS guide notes that at global scale one may add message buffering, durability services, and cross-system coordination, but caution that each component increases complexity.

Platform comparisons highlight trade-offs: monoliths (one service) have low latency and easy development but limited scalability, while microservices (many small agents or functions) scale well and isolate failures but introduce communication overhead and operational complexity. We reflect these trade-offs in our architecture comparison (Table below).

Observability, Safety, and Performance

For observability and safety, mature platforms follow best practices like the NIST AI RMF and OWASP guidelines. For example, Miruntime cites NIST’s AI Risk Management Framework as guidance for documenting evidence and policy decisions. OWASP’s Agentic Security Initiative lists threats like goal manipulation and runaway loops, recommending logging, validation, and fail-safes. Key mitigations include goal consistency validation (detecting and blocking unauthorized goal changes) and audit trails of all decision steps. We adopt these: the enhanced MIR would cryptographically seal evidence logs and verify that any agent-initiated goal modification stays within the predefined contract. Similarly, performance metrics shift from token-level throughput to task success metrics (first-try success, recovery rate, evidence completeness) as suggested by MiRuntime. We will apply such holistic metrics (from [10], [44]) in our evaluation plan.

3. Teleodynamics and Goal-Directed Systems

Teleodynamic theory (from biology and cybernetics) stresses that goal-directed systems maintain themselves. An autopoietic (self-producing) system continuously regenerates its components and preserves coherence amid change. In agentic AI, researchers describe recursive perception–action loops with an inner reflective loop: systems don’t just produce outputs, they observe and adapt their own state. Haidemariam et al. (2026) note that agentic architectures converge on an iterative loop of planning→acting→observing→evaluating→refining, forming “autopoietic circuits: self‑maintaining processes that preserve coherence amid flux”. Crucially, these agents hold internal goals that they monitor and update. The framework of synthetic teleology describes how artificial agents can generate and regulate their own goals through ongoing self-evaluation.

Key teleodynamic concepts for runtime design include:

• Goal hierarchies (Hierarchical teleology): Goals can be layered. Higher-level (meta-)goals oversee lower-level objectives, providing structure and preventing aimless growth. For instance, a top-level goal might be “maintain system stability”, under which subgoals address accuracy, cost, etc.

• Self-models and reflection: The system maintains a model of its own performance or policy. A reflective critic can suggest goal edits or strategy changes when deviations persist. This is akin to an inner loop that monitors drift from objectives.

• Intrinsic value/homeostasis: Inspired by biology, we can define internal “health” variables (e.g. resource usage, entropy of beliefs). The agent then has built-in drives to keep these variables within bounds (homeostasis). In MIR terms, one might introduce an intrinsic value function that penalizes, say, excessive context growth or too many tool calls. Active inference models (Friston) treat goals as priors and evaluate based on prediction/error, which mirrors a teleodynamic view of uncertainty-aware goals. Information-theoretic measures like empowerment bias agents toward states with high control potential.

• Maintenance cost (“no-op” control): Teleodynamic frameworks insist that any structural change be “paid for” by its maintenance cost. In practice, an agent should choose to do nothing if a proposed change doesn’t justify its complexity. Thus no-op is a valid, safe action when uncertainty is high, preventing runaway growth or spurious features. We will implement this by allowing the runtime to veto model-proposed actions when evidence is insufficient.

• Recoverability and resilience: Analogous to cellular repair, the system must detect failures (e.g. hallucinations, divergent loops) and trigger recovery (rollback, checkpoint) automatically. This ties into evidence schemas and error recovery components that MIR already envisions, but extended to be goal-aware.

• Adaptive focus (resource closure): Teleodynamics emphasizes resource-bounded learning. MIR should manage its own memory and compute budgets adaptively. For example, an Adaptive Focus Memory component (as proposed in Cruz 2026) could compress or prune context entries dynamically.

In sum, teleodynamics introduces internal drives and feedback to the runtime itself. We will map each concept to MIR: goal hierarchies to new data structures/APIs, homeostatic loops to monitoring services, intrinsic values to extended contract fields, and self-models to UX for developer feedback.

4. Mapping Teleodynamic Concepts into MiRuntime

Building on the above, we integrate teleodynamic ideas into MIR’s components and APIs:

• Goal Management Layer: Introduce a Goal Hierarchy component within the runtime. The Runtime Contract schema is extended to include subgoals or internalObjectives. APIs could include POST /runtime/goals to add or revise a subgoal, and GET /runtime/goals to inspect the current goal tree. The developer dashboard would display goal trees and success metrics at each level. Internally, the Task Planner uses this hierarchy to decompose user intent and to regulate subtask scheduling.

• Reflective Critic: Implement a background service (“Reflector”) that reads the Evidence Ledger during execution. It compares actual outcomes against the current goal hierarchy and success criteria. If misalignment is detected (e.g. a tool repeatedly fails a subgoal), the critic can propose a new subgoal or modify success thresholds. For example, an API POST /runtime/reflect could trigger a review of the current execution trace, potentially adjusting the execution.state or intent.successCriteria.

• Intrinsic Value Functions: Extend the authority or execution fields in the runtime contract to include intrinsicValue or riskTolerance parameters. These would quantify preferences for preservation (e.g. preferring no-op or partial solutions over risky actions). The Permission Policy component would check not only external policies but also this intrinsic value: e.g., block a destructive tool call if it violates an internal viability constraint. A conceivable API is GET /runtime/state/value returning the current intrinsic utility of ongoing execution.

• Homeostatic Loops: Add monitoring in the Runtime Orchestration layer for resource use (time, tokens, API budget) and model drift (e.g. perplexity growth). The contract’s execution.budgets already tracks step/time budgets. We build on this by auto-triggering checkpoints or rollback if metrics deviate. For instance, if memory usage exceeds a threshold without goal progress, the system may restart from the last checkpoint. The ErrorRecovery component is expanded to handle such teleo-failures, and new endpoints like POST /runtime/recover could be provided.

• Self-Maintenance Tools: Define tools for “clean-up” and “review” as first-class citizens. For example, a MemoryPrune tool that cleans stale memory entries according to evidence; or a HealthCheck diagnostic tool that summarizes internal state. These tools follow the usual MIR contract (typed I/O, permissions, rollback) but serve the runtime’s internal stability. They would be registered in the ToolRegistry and appear to the agent model as callable actions.

• Developer UX Patterns: Update MIR’s observability UI to include teleodynamic metrics. For example, show graphs of “teleological coherence” (alignment of goal updates with new evidence) or “goal-persistence” (stability of goals under perturbations) as per Haidemariam et al. These metrics inform developers whether the agent is being purposive. We may also add explicit user prompts: e.g. if the runtime’s “Reflector” suggests a new subgoal, the user (or admin) must approve it as part of the ApprovalQueue.

By concretely mapping teleodynamic constructs to MIR modules, we make the abstract ideas actionable. All new components follow MIR’s existing interface patterns: explicit schemas, logging to the Evidence Ledger, and being constrained by the Permission Policy. This ensures the enhancements remain compatible with the overall design.

5. Implementation Roadmap

We propose a phased roadmap to roll out these features. Each phase has specific milestones, effort estimates, risks, metrics, and example APIs/endpoints:

Milestones: Define schema for hierarchical goals in the runtime contract. Extend the data model and database to store subgoals. Implement basic API endpoints (GET/POST /runtime/goals). Add a monitoring module that logs goal state changes to the Evidence Ledger. Update the developer UI to display goal trees. Risks: Complexity of goal schema, conflicts with existing workflows. Avoid overengineering early. Evaluation Metrics: Goal Consistency (fraction of goal updates that remain aligned with user intent), Evidence completeness for goal-related events. Use synthetic tasks where the agent must decompose tasks. Example APIs: POST /runtime/goals (body: new subgoal JSON), GET /runtime/goals/status.

• Phase 1: Goal Hierarchies & Monitoring (Effort: Medium)

Milestones: Build the Reflector service that subscribes to the runtime’s event stream. Implement logic to compare execution logs against success criteria. Enable it to emit recovery actions (e.g. new checkpoint, goal adjustment) to the ApprovalQueue. Add resource-usage monitors (step count, token count) tied to the execution.budgets. Introduce a “no-op policy” where, if uncertainty exceeds threshold, model output is ignored unless reviewed. Risks: Ensuring that intervention doesn’t deadlock the workflow. Balancing false positives (over-triggering recovery) vs. misses. Evaluation Metrics: Recovery Rate (ratio of failures rescued by runtime), Task Latency (overhead of checks), Goal-Persistence under perturbation. Test with tasks that induce errors (e.g. context overflow) to see if runtime gracefully recovers. Example APIs: POST /runtime/reflect (analyze current state), POST /runtime/recover (apply recovery actions from reflector).

• Phase 2: Reflective Loop & Homeostasis (Effort: High)

Milestones: Integrate an IntrinsicValueFunction into the policy engine. Extend the intent and authority schema to include intrinsicValue and self-imposed constraints (e.g. “max external API calls”). Implement logging of this value in evidence. Hook it into planning so that high-cost proposals (from the model) are vetoed if they hurt intrinsic viability. Also implement normative constraints per design pattern (e.g. ethical filters as soft-goals). Finalize developer documentation and test end-to-end. Risks: Designing meaningful intrinsic metrics is speculative. Potential conflicts between intrinsic goals and user objectives. Evaluation Metrics: Teleological Coherence (alignment of goal edits with new evidence), Reflective Efficiency (reduction in error per reflection step), Normative Fidelity (rate of constraint satisfaction). Employ benchmarks from active inference or RL as baselines. Example APIs: GET /runtime/state/value (current intrinsic value score), POST /runtime/policy/update (update normative constraints).

• Phase 3: Intrinsic Values & Alignment (Effort: High)

Each phase includes security testing. For example, Phase 2 will verify that the Reflector cannot be bypassed by malicious prompts (per OWASP’s goal-manipulation mitigations). We track approval latency and failure injection recovery as metrics. By the end of Phase 3, MIR will support teleodynamic loops with corresponding UX, governed by contracts and policies.

6. Security and Safety Considerations

Misuse and Alignment: Introducing internal goal mechanisms raises new risks. The runtime’s ability to modify its goals or veto actions could itself be manipulated. We guard against this using OWASP-style controls: every goal update or no-op decision is logged in the Evidence Ledger with cryptographic integrity; external auditors (e.g. secondary agents or admin reviews) must approve significant goal changes. We also enforce goal consistency validation – the runtime checks that any proposed goal edit stays within the broader intent’s semantics. Rate limiting is applied so an agent cannot repeatedly modify its own objectives unchecked.

Intrinsic values: The intrinsic value function must align with human objectives. We avoid open-ended self-interest by hard-coding guardrails (e.g. irrevocable policy constraints) in the PermissionPolicy. For instance, we may disallow intrinsic preferences that would override critical safety policies or lead to resource exhaustion. MIR’s design already requires review for irreversible actions; we extend this to require reviews for any autonomous goal edits that could have systemic effects.

Monitoring: In line with NIST AI RMF recommendations, we maintain immutable logs of all agent decisions and their rationales. The Evidence Ledger is exportable for offline audit. Dashboards will highlight anomalies: e.g. if the agent’s intrinsic utility drifts unexpectedly or if it ignores user constraints. This complements run-time safety patterns (circuit-breakers, input sanitization) already implicit in MIR.

Recovery: If misalignment or misuse is detected at runtime, the system can recover via saved checkpoints. For example, if an agent’s behavior violates a policy, the ErrorRecovery component can roll back to the last safe state. The “homeostatic” monitors will trigger such rollback when key metrics (e.g. a penalty score) cross thresholds.

In summary, teleodynamic extensions are bounded by MIR’s existing governance: explicit contracts, layered approvals, and evidence-driven review. We follow agentic AI security best practices, ensuring that increased autonomy (self-goals, self-monitoring) does not compromise control or transparency.

7. Architecture Comparison

• Scalability: Microservice/agent architectures distribute load and can handle higher concurrency, whereas a monolith has a single service bottleneck. A hybrid splits off heavy subsystems (e.g. sandboxing, databases) to improve scaling modestly.
• Latency: Monoliths enjoy lowest latency (no RPC calls), while microservices may add network hops. Hybrids incur intermediate overhead.
• Ergonomics: Simpler stacks (monolith or thin API gateway) are easier for small teams; microservices require more DevOps and higher maintenance. AWS advises starting simple then adding components only as needed.
• Teleodynamic Support: A microservices approach best mirrors distributed teleology: separate agents/loops can maintain their own goals and self-heal. Monoliths can implement teleodynamic logic, but all loops share one process, risking entanglement. Hybrid allows isolating only the most complex loops (e.g. a separate Reflector service) while keeping simpler parts local.

8. Diagrams

flowchart LR
  subgraph User/Input
    U(User or Application)
    U -->|Intent/Query| App
  end
  subgraph MIR_Runtime["Machine Intelligence Runtime"]
    App[Application Layer] --> Orchestration[Runtime Orchestration]
    Orchestration --> Policy[Policy & Permissions Layer]
    Policy --> Memory[Memory & Context Layer]
    Memory --> Tools[Tool & Connector Layer]
    Tools --> Model[Model Adapter Layer]
    Model --> Evidence[Evidence & Telemetry Layer]
    Evidence -->|Reviewable Result| U
  end

This block diagram shows the MIR layers and flow: user input enters the Application layer, then proceeds through orchestration, policy checks, memory/context selection, tool invocation, and model inference, with evidence captured for later review. (Teleodynamic modules such as Goal Manager, Reflective Critic or Homeostasis Monitor would integrate alongside these layers, e.g. as sub-components of Orchestration and Policy.)

Further diagrams (not shown) could detail component interactions (e.g. sequence of API calls or data flows) and workflows for goal revision, but these provide the core view of the runtime’s structure.

Sources: MiRuntime reference materials and dev guide; AI runtime research; teleology/agentic AI literature; industry guides (AWS, LangChain); OWASP security guidelines.