Building Agentic Systems

An agentic system is a product where an AI agent can read shared state, reason about next steps, execute actions, and verify outcomes in a loop.

The key difference from a chatbot is mutation of real application state. Once an agent can change production data, architecture, guardrails, and observability become non-negotiable.

A reliable stack separates concerns across five layers: client UI, transport, API, runtime, and domain engine.

The domain engine is the source of truth for state, validation, history, and undo. The runtime orchestrates model turns and tool calls, but does not bypass domain rules.

All writes should flow through typed commands instead of direct mutation. Commands should return deterministic results, including an incrementing version number for sync integrity.

Tools should be explicit, schema-defined, and split into read and write classes. This enables stronger safety policies, clearer telemetry, and better loop controls.

Context strategy should start with a compact world summary, then load deeper state on demand. Most systems fail by overloading the model too early.

Runtime quality comes from a bounded gather-plan-act-observe loop with strict turn budgets, protocol repair, and compaction to keep long runs coherent.

Human-in-the-loop gates are essential for missing information and risky actions. The runtime must pause cleanly, preserve context, and resume from the same run state.

Production reliability depends on guardrails: stuck-loop detection, failure classification, retry budgets, and undo-first recovery paths.

Use streaming-first transport, stable event sequencing, and replayable cursors so clients can recover from disconnects without losing state.

Optimize cost and latency with prompt-prefix stability, diff-based context sync, and adaptive model routing based on task complexity.

The strongest agentic systems treat the agent as a normal client of the same product primitives humans use.

If commands are validated, tools are controlled, context is scoped, and runtime behavior is observable, agent autonomy becomes practical rather than fragile.