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Core Building Blocks

• Goal and constraints
  A clear objective with constraints (time, cost, permissions) gives the agent boundaries. Strong prompt design or a formal task schema helps the system reason cleanly about tradeoffs.
  
• Tool use
  Agents gain leverage by using tools—retrieval for background knowledge, code interpreters for precise computation, browsing for fresh information, and domain APIs to do real work. Tool outputs become new context for the next decision.
  
• Planning and decomposition
  Even simple models can accomplish more with explicit step-by-step plans. More advanced setups use dedicated “planner” components or planning prompts to structure work, track subgoals, and branch on contingencies.
  
• Memory
  Short-term memory holds the current plan and intermediate results. Long-term memory stores reusable facts, learned preferences, past resolutions, and artifacts. Good memory design reduces repetition and improves reliability.
  
• Reflection and self-critique
  Reflection prompts or separate “critic” models help agents catch mistakes, validate assumptions, and refine outputs. This can be as light as sanity checks or as heavy as unit tests and formal validations.
  
• Safety and governance
  Policies, permissioning, rate limits, and human-in-the-loop checkpoints ensure the agent only acts within authorized scopes. Observability (logs, traces, action histories) is crucial for debugging and accountability.
  

Why Agentic AI?

• Autonomy and efficiency
  Agents can handle multi-step tasks end-to-end, reducing human orchestration. They can run overnight research, triage tickets, generate drafts, and follow-up on blockers without constant supervision.
  
• Tool-augmented competence
  By calling calculators, compilers, search, and specialized APIs, agents sidestep LLM weaknesses and lean on systems designed for correctness and speed.
  
• Adaptivity
  Unlike static workflows, agents react to failures, missing data, or changing requirements—adjusting plans, trying alternatives, and escalating when needed.
  
• Reuse and scale
  Encapsulating workflows as policies and tools lets organizations scale patterns across teams and domains. Agents become templates for repeatable tasks.