Every enterprise runs on workflows — the repeatable sequences of steps that move work from request to completion. For decades those workflows have been deterministic: rules, checklists, approval gates, and human reviewers who click “approve” or “revise.”
But now, AI agents are making workflows smarter and faster by ingesting unstructured data, surfacing risk and priority, and preparing decisions for humans. As Box CTO Ben Kus puts it, “Historically, many different steps in these workflows were deterministic, sort of automatable steps,” and today, agents can add the missing intelligence those steps once required.
In the latest installment of the AI Explainer Series, Kus and host Meena Ganesh unpack what agentic workflows are, where they deliver the most value, real-world examples from customers, implementation guardrails, and practical steps to get started.
Key takeaways:
- AI agents turn unstructured data into prioritized, auditable recommendations so humans handle only high‑judgment decisions
- The best pilots are high‑volume, repeatable, and document‑driven (legal triage, compliance, vendor onboarding) — start small and measure
- Agentic workflows require precise specs, human‑in‑the‑loop approvals, explainability/audit logs, secure data handling, and iterative refinement
What is an agentic workflow?
An agentic workflow embeds autonomous AI agents into a business process so they can perform specific, repeatable steps that historically required human time and judgment. Kus explains the evolution: “In the history of all of these workflows across all these platforms, you’ve always had this challenge, which is that sometimes you need to apply intelligence in these workflows.”
Agents don’t replace humans; they augment them by reading documents, extracting data, applying checks, prioritizing items, and generating concise summaries that make human review faster and more focused.
As Kus said, “Imagine if you had an agent who would review all of the basics so that it could very quickly prompt you with the outcome.” In practice that means turning unstructured data — contracts, datasheets, proposals — into rich, actionable context that plugs directly into workflows.
Why now: The catalyst for adoption
Workflows have always existed, but many steps (especially those involving unstructured data) were manual because they required human reading and domain knowledge. Recent advances in generative AI and agent frameworks change that equation.
Kus describes this shift simply:
Now with generative AI, we have the ability to have agents participate in workflows and do some of these steps that used to pause and wait for somebody to get around to doing them.
Agents can now understand document semantics well enough to extract and normalize important data, follow instruction sets that codify business rules and audit requirements, and operate at scale to remove bottlenecks. The payoff: reduce grind, accelerate throughput, surface the right risks, and let humans spend time on the decisions that need judgment.
Transforming workflows across departments
Agentic workflows are especially powerful where the work is repetitive, document-driven, and where prioritization matters.
Some examples:
- Legal reviews: Legal teams have a long backlog of things to review but can’t efficiently surface the risky exceptions. By implementing a workflow where an AI agent reviews a series of standard steps, only items that are out of the ordinary are flagged for legal review. That pre-filtering saves time and highlights priority items.
- Product marketing and content reviews: Marketing teams routinely check datasheets and presentations for brand alignment and messaging consistency. Agents can verify guidelines and point marketers to areas needing attention, turning hours of reading into quick, prioritized actions.
- Loan origination and compliance checks: Agents can validate identity and documentation, flag incomplete or inconsistent materials, and assemble a dossier for a human to approve.
- Procurement and vendor onboarding: Agents can validate vendor forms, check for required certificates, and surface vendors requiring manual review due to nonstandard terms.
These examples share a common pattern: Agents reduce time-to-insight by digesting unstructured data and presenting prioritized, auditable findings to humans.
Key implementation principles and guardrails
As artificial intelligence continues to reshape business operations, AI agents are emerging as powerful tools for automating complex workflows. However, the path from AI experimentation to production-ready automation requires careful planning and strategic implementation.
Start small and build momentum
The most successful AI agent implementations begin with modest, well-defined processes rather than ambitious enterprise-wide transformations. Organizations should focus on identifying a known and repeatable process that teams can use to familiarize themselves with AI agent capabilities under human supervision, then gradually expand responsibilities over time.
Starting small reduces risk, enables faster learning cycles, and builds organizational confidence. Teams can observe how agents handle routine tasks, identify issues early, and develop best practices before scaling to more complex scenarios.
Define crystal-clear task specifications
Success with AI agents hinges on providing precise instructions about what the agent should accomplish. Organizations must clearly document what data to extract, establish specific success criteria, and define thresholds that trigger human intervention. This is like creating comprehensive task instructions, similar to training a new employee.
Before implementing full automation, teams should test individual steps in isolation to ensure the agent understands each component correctly. Clear specifications prevent misunderstandings and make troubleshooting easier.
Maintain human oversight for critical decisions
AI agents should not have authority over high-stakes decisions that could significantly impact the business. The recommended approach involves allowing agents to prepare data and provide recommendations while humans retain final approval, particularly in legal, compliance, or financial scenarios.
This human-in-the-loop model provides a safety net against AI errors while capturing automation benefits. It also builds trust between workers and AI systems, as employees see how agents support rather than replace their judgment.
Ensure transparency and auditability
AI agents must operate with complete transparency, logging actions, showing evidence, and explaining their reasoning. This visibility allows human reviewers to validate outcomes and understand decision-making processes. Organizations need secure platforms that provide clear explanations of AI reasoning for consistent verification and auditing.
Auditability becomes crucial in regulated industries where companies must demonstrate compliance and explain automated decisions to external stakeholders.
Implement robust security and governance
AI agents must operate within existing enterprise security frameworks, with appropriate access controls protecting sensitive information. This means integrating agent capabilities with current data protection measures, authentication systems, and content management policies while establishing clear boundaries around data access and usage.
Continuously improve through feedback
AI agent deployment should be viewed as ongoing refinement rather than one-time implementation. Organizations should systematically collect feedback from human reviewers to improve agent instructions and evaluation criteria. This iterative approach helps agents become more effective at identifying critical issues and directing human attention to the most important tasks.
The key to successful AI agent implementation lies in balancing automation benefits with human oversight, security, and transparency. Organizations following these principles will be better positioned to harness AI agent power while maintaining the control and reliability that enterprise operations demand.
Evaluating where to apply agents
When deciding which workflows to target first, evaluate opportunities against a few criteria:
- Volume and repeatability: High-volume, repeatable tasks yield the fastest operational ROI
- Document-driven: Workflows that rely heavily on unstructured content benefit most from AI comprehension
- Clear rules or heuristics: If you can express the business rules an agent should apply, it’s a good candidate
- Measurable outcomes: Pick processes where time saved, throughput, or risk reduction are easy to quantify
A good pilot is simple to scope, has a measurable baseline, and produces meaningful time savings when automated.
Piloting an agentic workflow in 6 steps
- Map the current process: Document steps, decision points, inputs, outputs, and current owners
- Identify candidate tasks: Choose a single, repeatable step involving unstructured content (e.g., contract pre-screening)
- Define evaluation criteria: Create a small annotated example set and a checklist the agent must follow
- Build a scoped agent: Implement an agent that performs checks, extracts fields, and produces a concise summary (plus risk flags)
- Run under supervision: Route outputs to human reviewers and capture feedback and corrections
- Measure and iterate: Track accuracy, time saved, and reviewer satisfaction, then refine instructions and expand scope as confidence grows
Kus summarizes the opportunity neatly: “This is a major new area of innovation — a new area where AI agents are beginning to have more of an impact on enterprises.” The goal isn’t to replace judgment. It’s to elevate it by removing repetitive barriers and surfacing what truly requires human attention.
Catch the full episode
For companies interested in driving efficiency and accelerating business, agentic workflows are a game changer. Watch the full episode to discover how AI agents are transforming the enterprise.
