What are agentic workflows and systems?
Agentic workflows and systems vs. chatbots and RPA
An agentic system is the full environment that makes safe, useful automation possible. It includes people and approvals, governed data, policies, tools, and the operations needed to run and monitor everything. An agentic workflow is the repeatable loop an agent follows inside that system to reach a goal: it gathers context, decides the next step, takes an action with an approved tool, checks the result, and repeats until done or escalated. This is different from a chatbot, which mainly answers questions and doesn’t reliably take actions, and from RPA, which executes fixed, rules-based steps. Agentic workflows adapt as conditions change but still operate inside guardrails the business defines.
Agentic workflow: a powerful loop
Agentic workflows follow a simple loop: perceive → plan → act → learn. Here’s a breakdown of each stage:
- Perceive: The agent gathers context: the request, relevant records, current status, and policies.
- Plan: It proposes the next best step and selects a permitted tool (SQL, API, application action).
- Act: It executes the step and records what happened.
- Learn: It checks the result against rules and data, updates state or memory, and decides what to do next—or hands off to a human when required.
This loop is powerful because it supports both predictable paths and the “messy middle” where inputs vary. The system supplies the oversight that keeps the loop safe: approvals for irreversible steps, cost and rate limits, and full audit trails.
Why now: pressure to automate cross-tool work with oversight
Teams are asked to do more with the same headcount. Many processes hop across tools (CRM, ticketing, finance, data warehouse) and stall on handoffs. Agentic workflows reduce the back-and-forth while keeping humans in control. They shine where variability is high, data is available, and the business cares about measurable outcomes like time to resolution, first-contact resolution, or cost per completed task.
Business use cases for agentic workflows and systems
Customer operations: case assembly, triage, next best action
Agents can gather documents, summarize details, cite the relevant policy, and draft a response or next step. For irreversible actions (e.g., account changes, refunds), the workflow pauses for a quick human approval with all context attached. The result is shorter handle time and higher first-contact resolution, without compromising controls.
IT and support: ticket routing, knowledge lookups, safe auto-fixes
Agents classify tickets, look up known fixes, and propose or perform low-risk remediations (e.g., restart a service, clear a cache) with automatic rollback if needed. Clear rules determine when to escalate. Benefits include smaller backlogs, faster p95 resolution, and more consistent application of best practices.
Finance and risk management: exceptions, reconciliations, alert review
Agents assemble evidence across systems, compare it to policy, and propose a disposition for review. Every step—and who approved it—is logged for audit. This reduces manual touches, rework, and policy violations, while improving traceability.
Retail and supply chain: pricing checks, demand signals, replenishment
Agents monitor demand signals, compare prices to rules, flag anomalies, and recommend actions. Teams move from reactive fire drills to steady improvements in cycle time, in-stock rates, and margin protection.
Agentic frameworks
Do I need a framework?
Not every problem needs an agentic framework; sometimes a simple workflow is enough. If the steps are stable and the inputs rarely change, a traditional workflow or prompt-and-approve pattern can be faster to build and easier to operate. Use the simplest approach that meets the business goal with acceptable risk.
A framework is useful when you need reliable tools, short- and long-term memory, and the ability to recover from failures or resume work after an interruption. Built-in tracing and evaluation are equally important: you want to see which tools were called, why a decision was made, and how much the run cost.
Selection criteria
When selecting an agentic framework, consider the following:
- Orchestration style: support for state machines/graphs so you can add checkpoints and retries
- Security and compliance: Least-privilege access to tools and data, plus policy enforcement
- Integrations: Easy hooks into your data platform, analytics, and operational systems
- Operations: Versioning, approvals, monitoring, and cost controls as first-class features
- Portability: Flexibility to bring your own models and switch providers without a rebuild
Reference architecture at a glance
Control plane: orchestration/guardrails, human-in-the-loop checkpoints
This is the “traffic controller.” It runs the perceive > plan > act > learn loop, applies policies, and routes approvals. It must support pauses, retries, and rollbacks so teams can trust the system in production.
Data plane: governed data access, retrieval/memory, least-privilege tools
Agents should read and write only what they are allowed to. Governed access to structured and unstructured data minimizes risk and improves accuracy. A vector-based memory helps the workflow recall relevant facts without duplicating or scattering data. Tools (SQL, APIs, actions) are whitelisted and described clearly so the agent can call them safely.
Ops plane: monitoring, evaluation, rollback/audit, cost controls
Operations glue makes agentic work sustainable. Teams need per-run traces, dashboards of success and failure, alerts for drift or cost spikes, and the ability to revert to a trusted configuration. Treat prompts, tools, and policies as versioned artifacts with approvals and change history.
Patterns you can ship now
Planner > executor (single agent + tools)
Start with one agent that breaks a task into steps and uses allowed tools to complete them. Insert checkpoints where the cost or risk is higher. This pattern is easy to reason about and often covers a surprising amount of real work.
Supervisor + specialists (planner, retriever, analyst, QA)
When tasks clearly benefit from roles, introduce a coordinator agent that delegates to specialists. For example: a Retriever that finds facts, an Analyst that writes SQL or calculations, and a QA agent that checks results. Use this pattern to scale complexity after the single-agent pilot proves value.
Checkpoints for irreversible actions (approvals, rollback)
For anything that changes customer data, finances, or configurations, human approval is required. Provide a compact, well-structured summary—what the agent intends to do, why, and a link to evidence—plus a one-click rollback for safety.
Build it with Teradata solutions
Teradata VantageCloud Lake and Enterprise Vector Store
Keep agents grounded in trusted data so they cite facts instead of guessing. VantageCloud Lake centralizes governed data access, while the Enterprise Vector Store provides efficient memory and retrieval without scattering copies across tools. Together they reduce errors and keep data policy in one place.
Low-friction connectivity
Expose safe, well-documented tools that agents can call—SQL for analytics, approved APIs for actions, and application connectors where needed. Teradata’s offers Bring Your Own Model capabilities so the system can evolve without lock-in. And Teradata’s MCP server provides standardized connectivity, enabling teams to add or swap capabilities without rewriting workflows.
ClearScape Analytics® ModelOps
Treat prompts, policies, tools, and models like code. With ModelOps practices including versioning, approval gates, run-time monitoring, and audit trails, you get observability, repeatability, and control. When something drifts or breaks, you know exactly what changed and can roll back quickly.
Evaluating your agent
Essential KPIs
Define “good” before launch and measure it from day one. Task success tells you if work gets done. Groundedness/quality shows whether outputs are supported by data and policy. p95 latency keeps customer and internal SLAs honest. Cost per task protects budgets. Escalation rate highlights where the agent still needs help.
Tracing and replay
Every run should produce a clear trace: inputs, tools called, decisions made, costs incurred, and final outcome. Use replay to debug and improve. Schedule red-team tests for failure modes (missing data, ambiguous requests, bad inputs). Version everything—prompts, policies, tools, and datasets—so you can compare apples to apples and comply with audit requirements.
Getting started with agentic systems and workflows
Unsure where to start? Follow this six-step checklist.
- Pick one process with measurable pain (e.g., backlog, SLAs, cost). Choose something bounded and visible, like “reduce time to assemble case files” or “cut ticket triage backlog 20%.”
- Define guardrails (what it may or may not do; HITL for high-risk steps). Clarify allowed tools, data scopes, and where a human must approve.
- Connect governed data + vector memory; define tool schemas. Keep data in-platform; give the agent clean interfaces for SQL/APIs/actions.
- Choose a fit-for-purpose framework. Start simple and add roles only if needed. A single agent plus tools is often enough for the first win.
- Instrument tracing/evals from day one; set baselines. Capture pre-pilot metrics and compare weekly. Track success, quality, latency, cost, and escalations.
- Pilot in sandbox; productionize via ModelOps once metrics hold. Harden approvals, rollback, rate limits, and budget caps. Document handoffs and on-call procedures.
Risk and governance
To ensure agentic systems operate safely and responsibly, consider implementing the following governance and risk management practices:
- Reliability and safety: Create approvals on irreversible actions; rollback paths.
- Cost control: Create budget caps; monitor cost per completed task.
- Compliance and audit: Log decisions, prompts, tool calls, and state.
- Change control: Version everything; require approvals for updates.
- Data handling: least-privilege access, masking where needed, retention policies.
- Operational hygiene: Create alerts for drift and errors; clear ownership and runbooks.
Conclusion
Agentic workflows promise speed and flexibility, but the win comes from pairing them with a responsible system: trusted data, clear guardrails, and strong operations.
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