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Agentic Engineering Status

Current focus

Agent Skills, Agentic Loops and Agent Architecture are all complete at first-draft depth. The Agentic Engineering workstream is now complete as a first-pass learning map from capability design through runtime control to production architecture.

Current state

  • AI Foundations is complete at first-draft depth.
  • Agent Skills is complete with 12 detailed first-draft topics.
  • Agentic Loops is complete with 12 detailed first-draft topics.
  • Agent Architecture is complete with 12 detailed first-draft topics.
  • Architecture coverage includes modular monolith, Hexagonal/Ports & Adapters, Clean Architecture, DDD/bounded contexts, context/state/memory boundaries, capability/tool architecture, RAG integration, security, queues, reliability, multi-agent trade-offs, evaluation and observability.

Decisions

  • Follow the progression capability → loop → architecture.
  • Treat agentic systems as normal software systems with probabilistic components inside explicit deterministic boundaries.
  • The model proposes semantic decisions; application/runtime code owns canonical state, lifecycle, authorization, budgets, approvals, side-effect guarantees and hard completion rules.
  • Keep context as a temporary purpose-specific projection rather than execution state or a database.
  • Distinguish domain/operational state, run state, session state, long-term memory and model context.
  • Treat tools as execution mechanisms behind application-owned capability/port contracts; keep provider SDKs and MCP/connectors in adapters.
  • Organize modules around business capabilities rather than AI providers/frameworks.
  • Prefer a modular monolith when it satisfies ownership, scaling, security and reliability requirements; extract services only for concrete boundaries.
  • Prefer deterministic workflows with bounded agentic islands where overall control flow is known.
  • Multi-agent architecture requires a real responsibility, permission, trust, scaling or ownership reason and should be evaluated against a simpler baseline.
  • Use retrieval for reference knowledge and live tools/application services for current operational truth.
  • Treat retrieved/tool content as data/evidence rather than trusted control-plane instruction.
  • Long-running runs should use durable state, queues/event-driven resume, checkpointing, idempotency/reconciliation and explicit retry budgets.
  • Evaluation and observability must cover the full trajectory: context, model calls, policies, capabilities, state transitions and final outcome.

Completed

  • Agent Skills topics 1–12 — first detailed drafts complete.
  • Agentic Loops topics 1–12 — first detailed drafts complete.
  • Agent Architecture topics 1–12 — first detailed drafts complete.

Next steps

  • Review/refine Agent Skills, Loops and Architecture as questions arise.
  • Validate the patterns through implementation examples and architecture exercises.
  • Choose the next separate AI workstream deliberately: RAG, MCP or Memory are the main candidates.