Generative AI produces outputs (content).
Agentic AI produces outcomes (actions + verification).
In practice, this is less about two competing “types” of AI and more about two different operating modes you can build on top of modern foundation models.
High-Level Comparison
| Feature | Generative AI | Agentic AI |
|---|---|---|
| Primary Goal | Knowledge synthesis | Task completion |
| Human Role | Constant prompting and correction | Goal setting and oversight |
| Connectivity | Isolated (usually) | Integrated (APIs/tools) |
| State Handling | Mostly stateless unless you re-provide context | Maintains contextual state across a long-running workflow |
| Error Handling | Needs the user to correct and re-prompt | Self-corrects via feedback loops (within guardrails) |
| Analogy | The consultant | The operator |
Generative AI (The Creator)
What it is: A model that takes a prompt and generates content—text, images, code, audio, and similar artifacts.
Core behavior:
You ask → it answers/creates → it stops.
Mental model: A very capable “autocomplete” engine for knowledge work. Excellent for drafting and synthesis, but it does not inherently run a process end-to-end.
State (Why This Matters in Networking)
Network engineering is state-driven: routing tables, neighbor adjacencies, session tables, counters, telemetry baselines, and time-correlated events.
Generative AI is effectively stateless from an operational standpoint. It does not know what happened five minutes ago unless you provide that context again (logs, snapshots, outputs) in the prompt. That’s fine for writing and analysis, but it’s not a control-plane for a workflow.
Example (Work Context)
You say:
“Write a change plan for migrating the Internet edge from legacy routers to new hardware with minimal downtime.”
Generative AI can produce a strong first pass:
- scope and assumptions
- pre-checks and rollback plan
- high-level runbook steps
- risk areas (BGP convergence, asymmetric routing, NAT/state, DNS dependencies)
- validation checklist
But it typically will not (without you driving each step):
- log into systems and collect current-state configs
- validate live routing tables and neighbor state
- run pre/post change tests and reconcile results
- iterate automatically based on failed checks or unexpected outputs
Agentic AI (The Doer)
What it is: A system that can take a goal, plan a sequence of steps, use tools (APIs, browsers, ticketing systems, scripts, config repositories), check results, and keep going until the objective is completed or it hits a real constraint.
Core behavior:
You give a goal → it executes a multi-step workflow → it iterates until done (or blocked).
Mental model: A junior operator that can execute a runbook with supervision, not a fully autonomous engineer. The “automation” is the point, but the guardrails are the architecture.
State (Long-Running Workflows)
Agentic AI maintains contextual state across a task. That includes what it already checked, what outputs it collected, which steps succeeded, which steps failed, and what it plans to do next.
Architecturally, that state can live in:
- a workflow engine
- structured memory (task logs, artifacts, intermediate results)
- systems of record (tickets, CMDB, Git, runbook evidence)
This is why agentic systems can be effective for operational workflows: they can behave like a process with continuity, not just a one-time response generator.
Example (Work Context)
You say:
“Prepare me for a maintenance window to migrate BGP peers to new edge routers and validate success.”
An agentic system might:
- ask for scope: devices, peers, ASN, routing policy, and success criteria
- pull configs from a repo or device APIs (if authorized)
- generate a detailed MOP aligned to your standards
- collect baselines (neighbors, routes, BFD state, interface errors, platform health)
- run post-checks and compare deltas against baselines
- raise exceptions for anomalies (route loss, flaps, unexpected path changes) and propose remediation
- document evidence and summarize outcomes for the change record
Key Difference: The Control Loop
Agentic AI is distinguished by an explicit feedback loop:
Plan → Act → Observe → Adjust
Generative AI typically does not run this loop on its own. It responds to prompts; it does not operate as a closed-loop system unless you wrap it in orchestration.
Safety & Governance (Blast Radius)
This is the section that matters most from an architect perspective: permissions and blast radius.
Generative AI risk profile: Generally low operational risk, because it usually produces text. Worst case, it hallucinates a command in a document and a human runs it without thinking. That is still dangerous, but the AI itself is not executing changes.
Agentic AI risk profile: Higher operational risk, because it can hold API keys, credentials, and tool access. If you give it write access, you are effectively delegating action capability. That expands the blast radius dramatically.
Architectural requirements for agentic AI in production environments typically include:
- RBAC and least privilege: scoped credentials, short-lived tokens, and segmented access
- Guardrails: allowed actions, disallowed actions, and policy enforcement (think “automation ACLs”)
- Human-in-the-loop checkpoints: approval gates before any write action or high-impact change
- Auditability: immutable logs of actions, inputs, and outputs
- Change control alignment: ticket linkage, evidence capture, rollback readiness
If you remember one thing: agentic AI is not “just a smarter chatbot.” It is an execution layer. Treat it like automation that can think.
Concrete Side-by-Side Example (Incident Response)
Your goal: “Help me troubleshoot intermittent packet loss impacting a public-facing application.”
Generative AI:
- proposes hypotheses (congestion, MTU/fragmentation, asymmetric routing, policing, DNS/Anycast behavior, load balancer timeouts)
- suggests a troubleshooting flow and command sets
- helps draft stakeholder updates and a postmortem outline
Agentic AI:
- collects telemetry from authorized tooling (interface counters, logs, flow data, synthetic probes)
- correlates timestamps across domains (edge, firewall, load balancer, app)
- performs automated RCA by traversing the OSI stack: physical signals and errors, then L2/L3 adjacency and routing, then L4 session behavior, then L7 health and timeouts
- identifies the discrepancy, proposes remediation steps, and captures evidence
- iterates until resolution or a hard dependency blocks progress
Current Limitations (Why Supervision Still Matters)
Agentic systems are improving quickly, but today they still require tight controls in production. The right mental model is:
“A junior engineer with supervision.”
They can be extremely effective in well-defined workflows with clear success criteria, structured inputs, and constrained actions. They should not be trusted with broad permissions or ambiguous objectives without approval gates.
Architect’s Takeaway
Generative AI drafts.
Agentic AI executes.
From a network architecture lens, the real difference is state, tooling integration, and governance. If you give something the ability to act, you must design for blast radius, auditability, and safe failure modes.