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AI agents (the focused doers)
A single autonomous software worker that can perceive inputs, reason over context, and act through tools/APIs to finish a well-defined task. Typical traits: autonomy within scope, task-specific design, and reactivity/adaptation to changing inputs. Think: support bot with RAG, an email triage assistant, or a calendar coordinator.
Agentic AI (the coordinating crew)
A system of multiple specialized agents plus an orchestration layer. They decompose goals, talk to each other, share memory, and coordinate actions. Think: a research pipeline with Planner → Retriever → Summarizer → Critic → Drafter; or a smart-home that pre-cools based on weather, price signals, and your calendar.
Quick difference table
| Dimension | AI Agents | Agentic AI |
|---|---|---|
| Unit | Single agent | Multi-agent system |
| Trigger | Prompt/goal triggers a bounded task | High-level goal triggers planning + delegation |
| Memory | Optional short-term or per-task memory | Shared, persistent memory across roles |
| Planning | Short horizon (“few steps”) | Long horizon; dynamic re-planning |
| Coordination | Not required | Essential (orchestrator / protocols) |
| Best for | Email triage, returns bot, scheduling, internal search | Research automation, multi-robot coordination, complex ops |
Examples
1) AI Agent – executive-assistant scheduler
- Input: “Find 45 minutes for a follow-up with Product next week.”
- Steps: parses intent → checks calendars → proposes slots → books → sends Slack updates → learns I avoid Friday 8 a.m.
- Why it fits: narrow scope, reliable APIs, short-lived state.
2) Agentic AI – literature review autopilot
- Roles: Planner breaks goal → Retriever pulls papers → Summarizer drafts → Critic checks claims → Drafter assembles → Editor polishes.
- Shared memory tracks findings and unresolved questions. Orchestrator assigns tasks and merges results. Ideal for long-horizon work with many moving parts.
3) Agentic AI – smart operations
- Supply chain: agents watch inventory, lead times, pricing, logistics; orchestrator reconciles plans and raises interventions.
When to use which (decision checklist)
Choose AI Agent if:
- The task is well-scoped, success can be verified with a single API/database check, and context fits in one session.
- Latency and cost matter more than full autonomy.
- Failure modes are simple and local (retry, escalate, or ask human).
Choose Agentic AI if:
- Objectives require goal decomposition, multiple tools, or domain roles.
- You need state over time (days/weeks) and explainable hand-offs.
- The organization wants parallelism and resilience to sub-task failure.
Guardrails and gotchas
- Hallucinations & brittleness: mitigate with RAG, schema checks, and verification prompts.
- Coordination failure: define roles tightly, route via an orchestrator, and use shared memory with audit logs.
- Emergent behavior / drift: add reflexive self-critique, constraints, and monitoring.
- Explainability: keep reasoning traces, decision logs, and source attributions.
Implementation playbook (adapt to our nnnmic.com flows)
A) Ship a single AI agent
- Inputs: prompt template + typed schema; guardrails for safety.
- Tools: HTTP/API nodes for internal systems; vector search for RAG.
- Loop: ReAct-style “think → act → observe” with a max-step cap and fallback to human-in-the-loop.
- Logs: Persist inputs, tool calls, outputs, and latency metrics.
B) Scale to Agentic AI
- Roles: define Planner, Retriever, Synthesizer, Critic, Executor.
- Orchestrator: a controller that assigns tasks, enforces schemas, and reconciles outputs.
- Memory: shared vector store (facts), episodic store (events), semantic store (facts/rules).
- Protocols: message contracts between agents; explicit acceptance criteria.
- Evaluation: task-success, factuality, coverage, stability across seeds, cost, and wall-time.
Example prompts
Planner: “You are the Planner. Break the goal into minimal steps. For each step, return role, inputs, tool, and acceptance_criteria. Use past memory if helpful. Do not execute.”
Retriever: “You are the Retriever. Given query and sources, return top-k chunks with citations and confidence. If confidence < threshold, re-query with a refined query.”
Critic: “You are the Critic. Check each claim against sources. Label each as {supported, weak, unsupported}. Suggest fixes.”
Executor: “You are the Executor. Call tools per step plan. Validate responses against schemas. On mismatch, retry or escalate.”
Metrics to track in our bi-weekly updates
- Success rate per task, verification pass rate, and manual-review rate.
- Mean steps per completion; % replans; time-to-resolution.
- Cost per run; cache/RAG hit rate; top failure categories.
Bottom line
- AI agents: single, tool-using workers optimized for a bounded job. Great at “do X, then Y, call this API, and report back.”
- Agentic AI: a team of specialized agents that plan together, decompose the goal, coordinate via memory/orchestrators, and adapt over long horizons. Built for complex, cross-tool, cross-step workflows.
Use AI agents for narrow workflows that need speed and reliability. Use Agentic AI when the problem needs multi-step planning, hand-offs, and shared context.
