If you’ve attended any enterprise software conference in the past twelve months, you’ve heard the word “agentic” approximately four hundred times. Every vendor, every platform, every startup is building “agentic AI.” It’s the buzzword of the moment, and like all buzzwords, it’s being applied so broadly that it’s starting to mean nothing.

So let’s cut through it. What’s actually working in agentic enterprise workflows right now, what’s promising but unproven, and what’s pure vapourware?

First, What “Agentic” Actually Means

An agentic workflow is one where an AI system doesn’t just respond to a single prompt — it breaks down a complex task into steps, executes those steps autonomously (or semi-autonomously), uses tools and APIs along the way, and handles errors and edge cases without constant human intervention.

The key distinction from a standard LLM interaction is autonomy and tool use. A chatbot answers questions. An agent does work.

Simple example: you tell an agent “prepare a quarterly sales report for the APAC region.” The agent queries your CRM, pulls data from your finance system, generates visualisations, writes a summary narrative, and compiles it into a slide deck. You review and approve the output rather than doing each step yourself.

That’s the promise. The reality is more complicated.

Where Agentic Workflows Are Actually Delivering

Customer support triage and resolution. This is probably the most mature agentic application in enterprise settings. Systems from Intercom, Zendesk, and newer players like Sierra are handling a meaningful percentage of customer queries end-to-end. The agent reads the ticket, checks the customer’s account, applies relevant policies, and either resolves the issue or escalates with full context. The best implementations are resolving 40-60% of tier-one tickets without human involvement.

Code generation and development workflows. Tools like GitHub Copilot Workspace, Cursor, and Claude Code are moving beyond autocomplete into genuine agentic territory. Developers describe what they want in natural language, and the system generates code, writes tests, and iterates based on feedback. Team400.ai has been working with organisations deploying these agentic coding tools at scale, and the productivity gains are real — particularly for boilerplate code, test generation, and documentation.

Data pipeline management. This is a less glamorous application but a genuinely useful one. Agentic systems that monitor data pipelines, detect anomalies, diagnose failures, and either fix them automatically or propose solutions are saving data engineering teams significant time. The agents don’t replace data engineers, but they handle the routine monitoring and troubleshooting that used to consume hours of each day.

Document processing and compliance. Insurance claims, legal document review, regulatory filings — workflows where the agent reads documents, extracts structured data, cross-references against rules, and flags issues for human review. This is working well because the scope is constrained and the rules are relatively well-defined.

Where It’s Promising but Unproven

Multi-system orchestration. The vision of an agent that coordinates across Salesforce, SAP, Slack, and your internal tools to complete complex business processes is compelling. Some early implementations exist, but reliability drops sharply as the number of systems and decision points increases. Every API call is a potential failure point, and agents aren’t yet great at recovering gracefully from unexpected states.

Strategic analysis and decision support. Agents that synthesise data from multiple sources to provide strategic recommendations are interesting but difficult to validate. How do you evaluate whether an AI agent’s market analysis is good? The feedback loop is too slow and too noisy for rapid improvement.

Autonomous meeting management. Several products now claim to handle meeting scheduling, preparation, note-taking, and follow-up actions autonomously. The scheduling part works reasonably well. The “autonomous follow-up” part — where the agent sends emails, creates tasks, and updates project plans based on meeting content — is still hit-and-miss. One misinterpreted action item and you’ve got an agent sending incorrect instructions to your team.

What’s Still Vapourware

Fully autonomous business process automation. The pitch that you can point an agent at an entire business process — say, end-to-end procurement — and have it run autonomously is not realistic with current technology. The edge cases are too numerous, the judgement calls too nuanced, and the cost of errors too high.

Self-improving enterprise agents. The idea that agents will automatically improve their own performance over time by learning from their interactions sounds great. In practice, most enterprise environments have strict controls around model behaviour changes, and unsupervised learning in production is a compliance nightmare.

Agent-to-agent ecosystems. The concept of different AI agents from different vendors communicating and collaborating to complete complex tasks is architecturally interesting but practically nowhere near production readiness. Standards don’t exist, trust frameworks are immature, and debugging multi-agent interactions is extraordinarily difficult.

The Patterns That Work

Looking across successful agentic deployments, a few patterns emerge:

1. Constrained scope wins. The best agentic workflows operate within clearly defined boundaries. They know what they can do, what they can’t, and when to escalate. Trying to build a general-purpose agent that handles everything is a recipe for unreliable behaviour.

2. Human-in-the-loop, not human-out-of-loop. The most effective implementations keep humans involved at critical decision points. The agent does the preparation work and presents options; the human makes the call. Fully autonomous agents sound impressive in demos but make enterprise buyers nervous for good reason.

3. Observability is non-negotiable. You need to see what the agent did, why it did it, and what data it used. Black-box agents won’t survive enterprise procurement processes. The LangSmith observability platform and similar tools are becoming essential infrastructure.

4. Start with cost and time savings, not revenue generation. Agents that save employees time on repetitive tasks have a clear, measurable ROI. Agents that supposedly drive revenue through better customer engagement are much harder to attribute and justify.

The Honest Assessment

Agentic AI is a real and important evolution in enterprise software. But we’re in the early innings. The technology is genuinely useful for specific, well-defined workflows today. The broader vision — where agents handle complex, multi-step business processes with minimal human oversight — is probably three to five years away from being reliable enough for mainstream enterprise adoption.

If someone’s telling you their agentic AI platform can automate everything today, they’re selling you a roadmap, not a product. The smart money is on targeted deployments with clear success metrics, gradual scope expansion, and realistic expectations about what agents can and can’t do right now.