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<h1 align="center">AISA Roadmap</h1>
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This roadmap
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It highlights how responsibilities and capabilities mature across layers as system scale, risk, and operational requirements increase.
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| AISA Layer | Learning-Oriented
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|-----------|----------------------------|---------------------------|
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| **LLM Foundation** | Basic model invocation, prompt engineering, structured outputs, small-scale RAG, safety prompting | Model routing and fallback strategies, instruction tuning, grounding and attribution requirements, cost–latency optimization, alignment
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| **Tool & Environment** | Simple tool calling, read-only APIs, local scripts, basic input/output schema validation | Secure tool registry, sandboxed execution, scoped permissions, idempotency guarantees, rate limiting, audited API interactions |
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| **Cognitive Agent** | Single-agent planning, task decomposition, short-term memory, basic reflection mechanisms | Persistent goal tracking, calibrated tool usage, explicit termination logic, governed memory policies, human escalation strategies |
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| **Agentic Infrastructure** | Sequential workflows, minimal orchestration, basic logging and monitoring | Workflow engines and state machines, multi-agent coordination, distributed tracing, centralized runtime budget enforcement |
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| **Evaluation & Feedback** | Offline tests, manual inspection, simple correctness or success metrics | Trajectory-level evaluation, continuous online monitoring, regression prevention, red-teaming, human-in-the-loop review |
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| **Development & Deployment** | Manual experimentation, prompt versioning, limited testing and rollback | CI/CD pipelines, staged rollout (canary and shadow deployments), A/B testing, automated rollback, artifact provenance |
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| **Governance, Ethics & Policy** | Awareness of safety guidelines and informal review processes | Policy-as-code
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<h1 align="center">AISA Roadmap</h1>
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This roadmap defines a progressive maturity path for adopting the AISA architecture, from learning-oriented systems to enterprise-grade deployments. It illustrates how responsibilities, controls, and capabilities evolve across layers as system scale, autonomy, risk, and operational requirements increase.
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| AISA Layer | Learning-Oriented Maturity | Enterprise-Grade Maturity |
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|-----------|----------------------------|---------------------------|
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| **LLM Foundation** | Basic model invocation, prompt engineering, structured outputs, small-scale RAG, safety prompting | Model routing and fallback strategies, instruction tuning, grounding and attribution requirements, cost–latency optimization, alignment controls |
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| **Tool & Environment** | Simple tool calling, read-only APIs, local scripts, basic input/output schema validation | Secure tool registry, sandboxed execution, scoped permissions, idempotency guarantees, rate limiting, audited API interactions |
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| **Cognitive Agent** | Single-agent planning, task decomposition, short-term memory, basic reflection mechanisms | Persistent goal tracking, calibrated tool usage, explicit termination logic, governed memory policies, human escalation strategies |
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| **Agentic Infrastructure** | Sequential workflows, minimal orchestration, basic logging and monitoring | Workflow engines and state machines, multi-agent coordination, distributed tracing, centralized runtime budget enforcement |
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| **Evaluation & Feedback** | Offline tests, manual inspection, simple correctness or success metrics | Trajectory-level evaluation, continuous online monitoring, regression prevention, red-teaming, human-in-the-loop review |
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| **Development & Deployment** | Manual experimentation, prompt versioning, limited testing and rollback | CI/CD pipelines, staged rollout (canary and shadow deployments), A/B testing, automated rollback, artifact provenance |
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| **Governance, Ethics & Policy** | Awareness of safety guidelines and informal review processes | Policy-as-code mechanisms, audit-ready telemetry, risk-tiered oversight, compliance gates, explicit accountability frameworks |
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