Generative AI turns input into content—text, images, code, audio—on demand.
Agentic AI uses generative models plus tools, memory, goals, and feedback loops to plan and act in the world (software or physical), not just generate content.
In practice, most real products are moving from “answer engines” (generative) to “autonomy engines” (agentic), but autonomy brings new failure modes, costs, and governance needs.
1) Definitions you can rely on
Generative AI
A system whose primary output is content. It maps an input prompt to an output—an answer, a paragraph, a picture, a segment of code, a melody. The “unit of value” is the quality of that content (accuracy, style, originality, helpfulness).
Agentic AI
A system that uses models like LLMs as a decision-making core to pursue goals through planning → acting → observing → adjusting. It calls tools/APIs, writes & runs code, interacts with software/people, stores and recalls memory, breaks tasks into steps, and self-corrects. The “unit of value” is task completion—did it achieve the goal safely, reliably, and within constraints?
Key idea: Agentic AI is not a new model class; it’s a system design pattern built on top of (and orchestrating) generative models.
2) A mental model that sticks
Think of Generative AI as a brilliant author—fast, fluent, and creative.
Think of Agentic AI as a capable project manager—it drafts, plans, delegates to tools, checks results, changes course, and delivers the outcome.
3) Side-by-side in one glance
4) Concrete examples
Customer Support
Generative: Drafts a helpful reply from the knowledge base.
Agentic: Reads the ticket, checks order status via API, issues a refund per policy, updates the CRM, then drafts the customer email and logs the resolution.
Data Analysis
Generative: Explains a chart or summarizes a dataset you uploaded.
Agentic: Writes a query, runs it, notices missing fields, fixes the query, visualizes results, writes a short brief, schedules a weekly job.
Software Development
Generative: Suggests code snippets.
Agentic: Clones the repo, runs tests, proposes a patch, opens a PR, addresses CI failures, summarizes the diff for review.
Marketing Ops
Generative: Produces an email campaign draft.
Agentic: Segments users, generates variants, A/B configures them in your ESP, monitors results, pauses under-performers, reports lift.
5) Anatomy of an agentic system
Goal formation: Interprets human intent (“Migrate CRM contacts into the new system by Friday”).
Planning: Breaks work into steps; may use chain-of-thought internally, task graphs, or planners.
Tool use: Calls APIs, runs code, manipulates files, or controls a browser/robot.
Observation: Reads results, errors, logs, UI states.
Reflection & control: Compares observations to goals, adjusts plan, avoids loops.
Memory:
Short-term: scratchpads for a single task.
Long-term: user prefs, facts, outcomes, learned procedures.
Policy & safety: Permissions, rate limits, guardrails, human-in-the-loop, auditability.
Outcome: Completion signal, artifacts (reports, PRs, tickets), or state changes in systems.
6) When to use which
Use Generative AI when:
The output is content: explanations, drafts, images, localized copies.
The cost/latency budget is tight and human executes the action.
You need controllable style and bounded scope.
Use Agentic AI when:
You need automation and state change across tools/systems.
Tasks need multi-step reasoning and retrieval/tooling.
You have clear policies, permissions, and observability to manage risk.
Hybrid reality: Most production systems blend both. E.g., an agent plans/acts, but uses a generative model to draft emails or code at each step.
7) Quality & evaluation: different scorecards
Generative AI
Intrinsic: factuality, coherence, style, toxicity, bias.
Task-linked: answer accuracy, ROUGE/BLEU for summarization/translation (with caution), human preference scores.
Agentic AI
Task success rate: % of goals completed end-to-end.
Safety: no unauthorized actions, adherence to policy, PII handling.
Efficiency: steps taken, tool cost, latency.
Robustness: recovery from errors, determinism under the same state, avoidance of infinite loops.
Traceability: logs, action history, reasoning summaries that are auditable.
Tip: For agentic systems, create simulated sandboxes and scripted scenarios to test failure modes before production.
8) Costs, latency, and ops
Generative: Mostly model tokens + occasional retrieval; predictable.
Agentic: Tokens plus tool/API calls, retries, planning overhead, and longer wall-clock time.
Observability: You’ll need action logs, tool results, and guardrail telemetry.
Caching: Aggressive caching and memoization can cut cost/latency for both, but especially for repeated agent steps.
Concurrency: Agents can fan out (parallel sub-tasks) but must handle rate limits and partial failures gracefully.
9) Risks & how to manage them
Hallucinations (both): Prefer retrieval-augmented prompts; validate with tools; never let unverified text drive high-impact actions.
Over-reach (agentic): Least-privilege permissions, explicit scopes, allow/deny lists, and human approvals for risky steps.
Infinite loops (agentic): Step caps, watchdogs, “deadline” prompts, loop detectors.
Prompt injection / tool misuse: Sanitize tool inputs/outputs; isolate untrusted content; verify actions against policy.
Data leakage: Redaction at input, classification at output, and environment segmentation.
10) Product strategy: a practical roadmap
Start with a great generative experience. Nail high-quality answers/drafts first.
Add retrieval & grounding. Reduce hallucinations and connect to your data.
Introduce tool use for low-risk tasks. Read-only APIs, reports, or analytics.
Add controlled write actions. Require confirmations or human approvals.
Graduate to semi-autonomy. Time-boxed runs, safe sandboxes, escalation paths.
Measure relentlessly. Task success, cost per completion, safety incidents, user trust.
11) Implementation checklist (copy/paste into your backlog)
Clear goal schema (inputs, constraints, success criteria).
Planner with fallback to “ask user” when ambiguity is high.
Tool registry with typed I/O, rate limits, and permission scopes.
Memory policy: what to store, retention, privacy.
Guardrails: PII filters, policy checks, action allowlists, approval workflows.
Observability: step logs, traces, artifacts, cost/latency dashboards.
Replay & simulation harness for agent runs.
Evaluation suite for both content quality and task completion.
Kill-switches: step caps, timeouts, rollback strategies.
User controls: transparency (“what I will do”), undo, consent.
12) The future: convergence, not competition
The boundary is fading. Tomorrow’s systems will look like:
Grounded, tool-using generators that can prove their claims (citations, calculations, code).
Well-governed agents that can explain their actions and generate human-grade artifacts along the way.
Orchestrated multi-agent teams specialized by skill (retriever, planner, executor, reviewer), with policies as first-class citizens.
Bottom line: Generative AI made machines fluent. Agentic AI makes them useful—safely moving from words to work.
Quick glossary
Generative model: Produces content tokens (text, image pixels, audio samples).
Agent: A goal-directed system that plans and acts via tools/APIs.
Tool use: External function calls—APIs, code execution, databases, search, RPA.
Memory: Short-term (task context) and long-term (facts, preferences, procedures).
Guardrails: Policies and controls that block, constrain, or review actions.
Task graph: A directed plan of steps/sub-steps with dependencies.
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