What Is Agentic AI? DataPeak’s Guide to the Next Wave of Automation
From Outputs to Outcomes
Generative AI made it easy to produce answers, summaries, and drafts. Useful, but mostly outputs.
Agentic AI moves beyond outputs to outcomes: agents that understand goals, plan steps, use tools, and execute tasks autonomously (with guardrails). That shift, from answering to acting, is the next wave of automation.
For organizations that live and die by decision speed and operational accuracy (hello manufacturing and the public sector), this isn’t hype. It’s table stakes. In this guide, we’ll define agentic AI, show how it differs from GenAI and classic automation, and explain how DataPeak makes it practical with no-code workflows, strong governance, and live connections to your data.
What Is Agentic AI? (Clear Definition)
Agentic AI is software that can:
Interpret goals (what you want),
Plan the steps to reach those goals,
Use tools and data across systems,
Execute actions and adapt when conditions change,
Report back and learn from feedback.
Where GenAI generates content, agentic AI orchestrates work. It reasons, sequences, and acts, often across multiple apps and datasets, while keeping humans in the loop.
Agentic AI vs. Generative AI (at a glance)
Generative AI: Produces text/images/code given a prompt (e.g., “summarize” or “draft an email”).
Agentic AI: Pursues a goal, decomposes it, calls tools/APIs, reads/writes data, triggers workflows, and confirms completion.
DataPeak AutoML & No-Code Agentic AI Demo Video
Why Agentic AI Now?
Data is everywhere, action isn’t. You already have dashboards, models, and reports. The gap is execution, getting from insight to action without manual hops.
Tool/API explosion. CRMs, ERPs, ticketing, spreadsheets, bespoke systems. Humans shouldn’t be the orchestration layer.
Speed-to-decision. Markets, supply chains, policy mandates, everything moves faster. Waiting on handoffs is now a competitive risk.
Governance pressure. You need automation that respects roles, permissions, audit trails, and compliance from day one.
Core Capabilities of Agentic AI
Goal understanding & planning
Agents translate outcomes (“reduce line downtime by 10%”) into steps (monitor KPIs, detect anomalies, open work orders).
Tool & data use
Agents query datasets, generate graphs, call APIs, write to systems, and export outputs (e.g., PDFs, dashboards, webhooks).
Context memory
They remember prior steps and results within a run (and, when appropriate, across runs), improving continuity and accuracy.
Self-evaluation & adaptation
If a step fails (e.g., field missing, token expired), agents adjust the path, request missing context, or escalate to a human.
Human-in-the-loop
Approvals, thresholds, and reviews at critical stages—so teams stay in control.
How DataPeak Makes Agentic AI Practical (No-Code)
Agentic AI only works at scale if non-technical teams can use it. DataPeak provides:
No-code agent builder: Drag-and-drop components (inputs, transforms, queries, AI steps, exports).
Multi-mode data fabric: Structured + unstructured data, unified behind a consistent interface.
Dashboards → Workflows: Visuals aren’t the end—they’re triggers (thresholds, alerts, assignments).
Forecasting → Actions: ML/ARIMA forecasts can auto-create tasks, requests, or orders.
Governance by design: Roles, hierarchies, tokens, audit trails—all native.
Outputs anywhere: Downloadable assets (PDFs/CSVs), embeddable graphs, output APIs for external systems.
Bottom line: People don’t just “see” the right thing in DataPeak, they do the right thing, with agents closing the loop.
Real-World Use Cases (Manufacturing & Public Sector First)
1) Manufacturing: Predictive Maintenance → Work Orders
Goal: Minimize downtime.
Agent plan:
Monitor machine health KPIs (temperature, vibration, rejects).
Detect anomaly vs. forecast.
Create maintenance ticket with priority, attach context (last service, part #).
Notify shift lead; if no acknowledgment in 15 minutes, escalate to plant manager.
DataPeak path: Forecasting module → dashboard threshold → agent triggers CMMS work order via output API → tracked in dashboard with SLA.
2) Food Processing: Quality Spike Response
Goal: Contain defects fast.
Agent plan:
Merge line data + QC inspections (AI Data Merge contextualization on product ID).
Identify spike windows; isolate batch range; generate recall checklist PDF.
Notify QA lead; update dashboard status tile to “Investigating.”
DataPeak path: Merge → auto-analysis graph → agent generates PDF + emails stakeholders → dashboard status updates.
3) Public Sector: Budget Oversight & Procurement
Goal: Prevent overruns, enforce approvals.
Agent plan:
Track spend vs. thresholds by dept.
If over 80%, notify budget owner; over 95%, freeze non-critical POs (policy-based).
Route exceptions for director approval; write decision back to system.
DataPeak path: Datasets + dashboards → thresholds → agent orchestrates emails, form validations (Data Sheets), and API writes to ERP.
4) Automotive Supplier: Collections Prioritization
Goal: Improve cash flow without adding headcount.
Agent plan:
Score accounts by DSO, history, dispute risk.
Draft outreach, schedule follow-ups, and update CRM status.
Produce weekly roll-up PDF for finance leader.
DataPeak path: Agent queries data → schedules comms → exports PDF summary → dashboards track DSO trendline.
Agentic AI vs RPA vs BI vs “Just GenAI”
RPA: Great for fixed, brittle steps. Struggles with change, context, and reasoning.
BI Dashboards: Great for visibility. Stop short of doing something.
GenAI: Great for content. Doesn’t inherently plan/act.
Agentic AI (DataPeak): Reasoning + planning + tools + data + action, inside governance.
Use them together: Dashboards to monitor, Forecasting to predict, Agents to act.
Governance, Safety & Control (The Non-Negotiables)
Roles & Hierarchies
Grant access by org structure. Sub-admins manage their scope; users see only what they should.
Data Sheets (with validation & history)
Operational forms with versioning, change logs, and admin validation for audit-proof trails.
Tokens & Output APIs
Granular, revocable access for external systems. Test harnesses ensure endpoints behave safely.
Human-in-the-loop
Approvals at key steps; agents present context (attachments, metrics) to speed confident decisions.
Auditability
Every action is attributable: who/what/when/why. Close the compliance loop without slowing work.
How to Start (Crawl → Walk → Run)
Crawl (1–2 weeks)
Pick one repetitive workflow (e.g., weekly KPI PDF, routine maintenance ticket creation).
Build a small agent in DataPeak’s no-code builder.
Add one approval step. Measure time saved.
Walk (1–2 months)
Promote high-value graphs to dashboards with threshold triggers.
Add forecasting to one key KPI.
Create 2–3 agents that hand off work between teams (e.g., QC → Maintenance → Reporting).
Run (ongoing)
Standardize agent templates across sites/departments.
Layer governance: sub-admins publish approved agents; users can run but not edit.
Instrument outcomes: DSO, MTTR, cycle time, cost-to-serve. Iterate quarterly.
Pro tips
Design for reuse: name conventions, descriptions, tags.
Prefer thresholds to manual checks.
Keep approvals where risk lives; automate the rest.
Review permissions quarterly.
FAQs
Q: Do we need data scientists to benefit from agentic AI?
A: No. DataPeak’s no-code canvas, dashboards, and forecasting let operations teams build and run agents. Data teams can still plug in advanced models when needed.
Q: How is this different from our current BI stack?
A: BI tells you what happened. Agentic AI does something about it—opening tickets, sending comms, updating systems, and generating artifacts automatically.
Q: What data do we need?
A: Start with one reliable dataset + a clear outcome. You can connect more sources (databases, APIs, files) over time via DataPeak connectors.
Q: How do we stay compliant?
A: Use Users & Hierarchies, Data Sheets (validation/history), and Tokens for output APIs. Every action is logged for audits.
Q: Where does GenAI fit?
A: Agents can use GenAI for text or summaries inside a broader plan, but the agent owns the multi-step workflow and the outcome.
What Good Looks Like (Signals You’re Winning)
Decision time ↓ (hours → minutes)
MTTR / downtime ↓
DSO ↓ and collections efficiency ↑
Rework/defects ↓; exception rate ↓
Human time reclaimed (ops/analysts) ↑
Fewer status meetings; more automated updates
Clear audit trails without extra admin work
Don’t Stop at Insight, Finish the Job
Generative AI helped teams move faster on content. Agentic AI helps organizations move faster on work. With DataPeak, teams don’t just see the right numbers; they trigger the right actions—safely, consistently, and at scale.
From predictive maintenance in manufacturing to budget controls in the public sector, the playbook is the same: connect your data, set thresholds, and let agents handle the repetitive orchestration while people focus on judgment and strategy.
Keyword Profile: DataPeak Agentic AI, DataPeak no-code AI, agentic AI workflows, what is agentic AI, agentic AI vs generative AI, no-code workflow automation, AI agents platform, predictive analytics to action