Agentic AI Tools for Brands

TL;DR — Key takeaways

Agentic AI tools don’t just generate text — they plan, decide, and execute multi-step workflows with minimal human input.
Klarna’s agentic deployment handles 2.3M customer conversations per month, cut resolution time from 11 to 2 minutes, and contributed $40M to profit.
Gartner forecasts 40% of enterprise agentic AI projects will be canceled by 2027 — mostly because teams picked tools before defining the workflow.
Only 23% of companies experimenting with AI agents have scaled beyond pilots (McKinsey, 2025). The bottleneck isn’t technology.
Brands that win with agentic AI start narrow, measure fast, and expand. The ones that lose try to automate everything at once.

Most brands that think they’re using AI aren’t using agentic AI. They’re using autocomplete. There’s a difference, and right now that difference is worth somewhere between a competitive edge and a company-level decision. The market for agentic AI tools hit $5.1 billion in 2024 and is projected to reach $47.1 billion by 2030, according to MarketsandMarkets. That’s not a trend. That’s a structural shift in how software works.

What surprises me isn’t the growth. It’s how many brand teams are still treating agentic AI like a fancier chatbot.

The Actual Difference Between AI Tools and Agentic AI Tools

Standard AI tools — including most implementations of ChatGPT, Gemini, and Claude in enterprise setups — work on a prompt-response cycle. You ask. It answers. You decide what to do with that answer. Every action still runs through a human.

Agentic AI tools break that cycle. They hold a goal, break it into subtasks, use tools (APIs, databases, browsers, code execution), and keep working until the goal is done or the task fails. The human sets the objective. The agent figures out the path.

Concretely: a standard AI tool can write a product description. An agentic AI tool can receive a product brief, pull competitor pricing from the web, analyze your historical conversion rates, generate three variants, A/B-test them via your CMS API, and report back — while you’re in a meeting. The outputs aren’t fundamentally different. The human-in-the-loop requirement is.

Here’s where most brands get it wrong: they evaluate agentic tools by the quality of their generated text. That’s like evaluating a project manager by their typing speed. The variable that matters is workflow completion rate — how many multi-step tasks does the agent finish correctly, without intervention?

Where Agentic AI Tools Are Actually Delivering Results

Two cases worth looking at in detail, because both are specific enough to be useful.

Klarna deployed an agentic AI system for customer service in early 2024. Within a year, it was handling 2.3 million conversations per month — equivalent to 700 full-time agents. Average resolution time dropped from 11 minutes to 2 minutes. Customer satisfaction scores held. The profit impact was $40M. Klarna’s own figures are publicly available and worth reading in full, because the internal resistance they describe is more instructive than the numbers.

Capital One ran a narrower test: agentic tools applied to lead follow-up sequences in their credit card acquisition funnel. Automated multi-touch follow-up, personalized by browsing and application behavior, without agent (human) involvement. Conversion on followed-up leads rose 55%. The insight wasn’t that AI is better at writing emails. It’s that speed and personalization at scale are impossible for human teams — and agentic tools eliminate that constraint.

40%

of enterprise agentic AI projects will be canceled before completion by 2027

Source: Gartner, 2024

Why 40% of Agentic AI Projects Get Canceled — and It’s Not the Technology

Gartner’s 40% cancellation forecast is the most useful number in this space right now, and it’s being almost entirely misread. Most coverage frames it as a technology risk. It isn’t. The tools work. The failure mode is organizational.

When we work with brand teams at Epinium, the pattern is consistent: companies invest in agentic tools before they’ve mapped the workflow the tool is supposed to replace. They buy capability first, then look for a problem. That’s backwards. An agentic tool that automates a broken process doesn’t save time — it accelerates the breakage.

The second failure mode is measurement paralysis. Agentic AI acts across multiple systems simultaneously. Attribution gets complicated. Teams that can’t clearly measure the agent’s contribution can’t justify continued investment, so the project stalls. What we see at Epinium is that the teams who succeed define one metric before launch — not five — and they instrument it before the agent goes live.

McKinsey’s 2025 State of AI report found 62% of companies are actively experimenting with AI agents. Only 23% have scaled a deployment beyond the pilot phase. That 39-point gap represents billions in stranded investment and an enormous execution opportunity for the brands that close it.

How to Evaluate Agentic AI Tools Without Getting Burned

Four criteria that separate useful from expensive:

Tool integration depth. An agentic system is only as capable as the tools it can use. Evaluate the native integrations before the interface. Can it write to your CMS, read from your analytics platform, call your e-commerce API? Most vendors demo on clean environments. Your environment isn’t clean.

Failure handling and escalation logic. Agents fail. What matters is whether the tool fails gracefully or silently. A good agentic system surfaces uncertainty, pauses when confidence is low, and routes to human review rather than guessing. Ask vendors: what happens when the agent hits an unexpected state? If the answer is vague, that’s a red flag.

Observability. You need to see what the agent did and why. Not just the output — the reasoning chain, the tools used, the decisions made. Without observability, you can’t improve the agent, catch errors, or maintain compliance. This is the feature most enterprise buyers undervalue and most regret ignoring.

Cost per task at scale. Demos are cheap. Production runs at volume aren’t. Get pricing for 10x your expected usage before you sign. Agentic systems with multi-step tool calls can consume API credits rapidly. Several well-publicized pilots stalled in 2024 specifically because cost projections were based on demo-scale usage.

The Comparison That Matters: Workflow Automation vs. Agentic AI

Capability	Workflow Automation (RPA/Zapier)	Agentic AI Tools
Task definition	Explicit, rule-based	Goal-based, self-planned
Handles ambiguity	No — breaks on edge cases	Yes — adapts to unexpected inputs
Multi-step reasoning	Pre-scripted only	Dynamic, context-aware
Learning over time	No	Yes, with feedback loop
Setup cost	Low — maps existing processes	Higher — requires workflow design
Best for	Stable, high-volume, predictable tasks	Complex, variable, judgment-heavy tasks
Competitive ceiling	Efficiency gain only	Can create capability competitors can’t match at speed

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Five Questions Brands Ask About Agentic AI Tools

What makes an AI tool “agentic” versus a standard AI assistant?

The technical boundary is autonomy over multi-step task execution. A standard AI assistant responds to a single prompt and waits for the next instruction. An agentic tool receives a goal and independently plans the steps needed to reach it — including using external tools like APIs, databases, browsers, or code executors. The practical test is whether the system can complete a meaningful workflow without human input at each decision point. AutoGPT, CrewAI, and LangGraph-based systems are examples of agentic frameworks. ChatGPT without tools enabled is not agentic, even though it’s still powerful.

Which industries are seeing the fastest ROI from agentic AI tools?

Customer service and e-commerce are currently ahead of every other sector, largely because they have the clearest ROI metrics: resolution time, conversion rate, cart abandonment. Klarna and Capital One are the most cited examples for good reason — both have published quantifiable results. Financial services and healthcare follow, but with significantly longer deployment cycles due to compliance requirements. B2B SaaS companies are emerging as the fastest movers in 2025, particularly for sales development, onboarding automation, and technical support.

How much does it cost to deploy agentic AI tools at enterprise scale?

Ranges vary enormously by architecture, but a realistic 2025 benchmark for a mid-size brand deploying one well-scoped agentic workflow: $15,000–$80,000 for initial setup (integration, testing, safety checks), and $2,000–$15,000 per month in ongoing API and infrastructure costs. Those numbers drop by 60–70% by year two as the workflow stabilizes and edge cases are handled. The ROI threshold is typically hit fastest in workflows where human labor cost per task exceeds $5 — customer service, content production, and data processing consistently clear that bar.

What are the biggest risks of agentic AI tools for brand teams?

Three risks dominate. First: hallucination in action — unlike a chatbot that gives a wrong answer you can ignore, an agentic tool that misremembers a fact might act on it, publishing incorrect content or sending wrong information to customers. Second: scope creep — agents that have access to too many tools develop unexpected behaviors; tight permission scoping is non-negotiable. Third: audit trail gaps — if you can’t reconstruct exactly what the agent did and why, you can’t comply with the EU AI Act, can’t debug failures, and can’t improve the system. The brands getting burned in 2025 are almost uniformly running agents without observability.

How do you measure whether an agentic AI tool is actually working?

Define one primary metric before launch. Not five. One. The most durable metrics are task completion rate (did the agent finish the workflow without human intervention?), error rate per task, and cost per task versus the human baseline. Secondary metrics like time-to-completion and customer satisfaction scores are valuable but shouldn’t replace that primary benchmark. Review weekly in the first 90 days. What we see at Epinium is that teams that try to measure everything measure nothing — and they can’t justify continued investment when budget reviews come around.

The brands that will look back at 2025 as a turning point aren’t the ones that deployed the most AI tools. They’re the ones that deployed the right ones, measured them honestly, and used those results to move faster on the next deployment. Agentic AI is still early enough that getting one workflow right puts you ahead of 80% of competitors. That window doesn’t stay open indefinitely. The technology is maturing fast, vendors are consolidating, and the brands building institutional knowledge now will have compounding advantages in 18 months that late movers won’t be able to close.

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