# Agentic Ai Engineering
All Agentic Ai Engineering notes →1. Perception
Covers how an agent ingests and represents its environment — structured tool outputs, unstructured text, multimodal inputs — and the encoding choices that determine what the planning stage can reason over.
2. Decision Making
Covers the decision-making layer that selects the next action from the perceived state — utility scoring, rule-based gating, and LLM-driven choice — and how confidence and risk thresholds shape when an agent should act versus escalate.
3. Planning
Covers agent planning strategies — task decomposition, hierarchical planning, and plan-and-execute versus ReAct-style interleaved planning — and the tradeoffs between upfront planning cost and adaptive replanning.
4. Reasoning
Covers the reasoning strategies an agent applies mid-execution — chain-of-thought, tree-of-thought, and tool-augmented reasoning — and how reasoning depth trades off against latency and token cost in a production loop.
5. Reflection
Covers self-evaluation loops where an agent critiques its own intermediate output before acting on it, the prompting patterns that implement reflection, and the measurable quality gains versus the added round-trip cost.
6. Self-Correction
Covers how an agent detects and repairs its own errors mid-task — retry-with-feedback loops, validator-driven correction, and the failure boundary where self-correction should hand off to a human instead of looping indefinitely.
7. Learning Loops
Covers how agents improve across invocations without full retraining — memory-based few-shot adaptation, prompt or policy updates from feedback signals, and the online-eval loop that turns production traces into improvement signal.
8. Agent State Machines
Covers modeling an agent's execution as an explicit state machine — states, transitions, and guards — as the pattern that makes agent behavior debuggable, testable, and resumable compared to an implicit prompt-driven loop.
9. Goal-Oriented Behavior
Covers how an agent maintains and decomposes a top-level goal across multi-step execution, tracks partial progress, and resolves conflicts between sub-goals without losing sight of the original objective.
10. Autonomous Execution
Covers the execution layer that carries a planned action through to completion without human intervention — action validation, rollback and compensation on failure, and the autonomy-level gating that determines how much an agent is trusted to do unsupervised.