What is agentic AI?
Definition and why it matters
Agentic AI refers to AI systems that don’t just answer questions—they pursue goals. First, an agent perceives context, plans the next step, and selects an approved tool (SQL, API, app action). Then it takes that action, checks the result, and decides what to do next. Because it can plan and act across systems, agentic AI tackles real business problems rather than stopping at a draft or suggestion. This capability is especially important in dynamic, complex environments where data is unpredictable, scenarios often deviate from the norm, and performance is judged by how quickly, accurately, and cost-effectively each task is completed.
Agentic AI vs. generative AI
Generative AI creates content from prompts, like summaries, emails, images, and code. Agentic AI adds planning, memory, and tool use so work gets done end to end: fetch data, update records, file tickets, draft and send messages, request approvals, and close the loop. In practice, these two types of AI pair nicely: generative AI produces drafts; the agent decides if and when to use them and ensures actions follow policy.
Agents vs. workflows and RPA
Workflow automation and robotic process automation (RPA) follow predefined, repeatable steps. They’re fast and reliable but can't adapt when inputs change. Agents are ideal for the “messy middle” where decisions depend on context and data, or where the next best action can’t be hard-coded. This makes agents better for variable, cross-system work. Winning teams blend both: deterministic flows for known paths, agents for variable work.
How agentic AI works
Core function and components
Each run follows a simple loop: perceive → plan → act → learn. The agent perceives the task and pulls context from governed data. It plans the next best step and chooses a permitted tool. It acts, then learns by checking results against rules and evidence. If the action is risky, the agent pauses for human approval; otherwise, it repeats until the goal is reached or escalates with a clear summary.
Core components include:
- Planning/orchestration: maintains state, chooses the next step, handles retries and fallbacks
- Tools and APIs: approved capabilities—query data, update CRM, open/close tickets, generate documents
- Memory: short-term state for the current task; long-term knowledge (e.g., vector search) to recall facts and past outcomes
- Guardrails: policies that define what the agent may do, with which data, and under what conditions; includes rate limits and budgets
- Observability: tracing for prompts, tool calls, inputs/outputs, costs, and outcomes so teams can debug, audit, and improve
Humans in the loop
Agentic AI keeps people in control. While it can run unattended, most teams start with human-in-the-loop approvals and expand autonomy gradually using policies, permissions, and audit trails. Common approval points include irreversible changes (financial transactions, PII updates), policy-sensitive actions, and high-cost operations. Approvals should be lightweight: the agent compiles evidence, proposes an action, and prompts a person to one-click approve or send back, with rollback if needed.
Business use cases
Customer support and customer experience (CX)
- Case assembly and triage: Pull history, policies, and logs; summarize facts; and propose a disposition with citations.
- Next best action: Draft responses, recommend credits/refunds inside policy; route exceptions for approval.
- Proactive care: Detect potential issues and notify customers with guided steps.
Impact: real-time predictive insights, lower handle time, higher first-contact resolution, consistent policy application
IT operations and security
- Incident triage: Classify and route, suggest playbooks, and run safe auto-remediations with rollback.
- Change validation: Gather diffs, assess risk, and queue approvals with context.
- SecOps assistant: Enrich alerts, map to runbooks, and generate action plans.
Impact: smaller backlogs, faster p95 resolution, better signal-to-noise
Sales and marketing operations
- Account research and briefs: Compile insights from approved sources and produce tailored summaries.
- Lead routing and enrichment: Validate, enrich, and assign to the right owner.
- Campaign ops: Check assets for brand/legal readiness and orchestrate updates.
Impact: more consistent, on-brand content, fewer manual updates, cleaner data
Data/engineering assistants
- SQL and analysis co-pilot: Generate queries against governed data, cite tables, and create quick summaries/visuals.
- Quality checks: Detect anomalies or schema drift, propose fixes, and open tickets with context.
- Docs and runbooks: Draft and maintain up-to-date technical documentation.
Impact: faster analysis cycles, fewer errors, better documentation hygiene.
Risks and considerations
Safety and ethics
Keep agents within agreed scope and guardrails. Use curated datasets, monitor outcomes for bias, and require explanations and citations for sensitive decisions. Default to reversible actions; require human approval when not.
Security and privacy
Enforce least-privilege access to tools and data with scoped credentials. Mask or tokenize PII, log access, respect retention and residency rules, and sandbox agents during testing. Prohibit unapproved external calls and encrypt secrets.
Technical limits and reliability
Mitigate hallucinations with grounding and validation checks. Track p95 latency and cost per task; set budgets and rate limits. Version prompts, tools, policies, and models; provide replay, rollback, and clear on-call runbooks.
Getting started
Quick-start steps
Pave the way for success by following these steps:
- Choose a bounded task with measurable pain (e.g., cut ticket triage time 20%).
- Define tool and data scopes. Whitelist exactly which tools/tables the agent may use and set read/write rules.
- Specify approvals. Identify irreversible steps and who approves them; make the evidence package clear.
- Turn on observability. Trace prompts, tool calls, inputs/outputs, costs, and outcomes from day one.
- Run a pilot. Start with a small cohort, compare against a control period, and gather user feedback.
- Harden and scale. Add rollback, rate limits, budget caps, and change control; document runbooks.
Success metrics
To measure success, start with key metrics such as the following:
- Task success rate: completed tasks ÷ attempts
- Attempts-to-success rate: average cycles to complete a task (lower is better)
- Time per task and p95 latency: end-to-end completion time and tail behavior
- Cost per task: tokens/compute/invocations per completed job
- Intervention rate: percentage of runs needing human help; log reasons to drive fixes
- Incidents: blocked or rolled-back actions; policy violations per 1,000 actions
Build vs. buy
Which option makes sense for your business? Here’s how to decide.
- Build when you need fine-grained control over data access, tools, and governance or must operate across clouds/models. Choose open, portable components.
- Buy (managed) when speed and integrations matter most and vendor-run reliability is a plus. Ensure you still get exportable logs, least-privilege access, and BYO-model options.
- Hybrid options allow you to operate your control/ops plane. Use managed services selectively (models, vector search, connectors).
Conclusion
Agentic AI moves AI from answers to action. With a simple, explainable loop and strong guardrails, teams can automate messy, cross-tool work without sacrificing safety or control. Start small, measure relentlessly, and scale autonomy only when the data shows it’s reliable.
Learn how AgentBuilder, Teradata’s enterprise-ready foundation for agentic AI, empowers enterprise leaders, data scientists, and AI practitioners to build and operationalize autonomous AI agents that deliver real business value.