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In an enterprise context, an AI agent is a specialized, generative-AI-powered resource built to take on one well-defined task inside a business process. Agents are configured using text-based prompts rather than code, so business teams can define and deploy them directly, without pulling in a data scientist or prompt engineer. At enterprise scale, dozens of teams may each need their own agent, so easy deployment eliminates any bottlenecks with IT.
Once deployed, an agent is embedded directly into the business process. As the process runs, it feeds the agent the information relevant to that step, and the agent acts on predefined rules and logic. Agents run inside the process itself, so they're always on, consistent, and auditable, using the same governance, security, and access controls that already apply to the rest of the process. We cover this in more depth in our AI agents blog, and in our broader look at enterprise AI.
Because AI agents are specialized rather than general-purpose, they tend to be deployed against a specific, repeatable job inside a process. Common examples include: • Content creation: drafting emails, documents, copy, and knowledge base articles • Categorizing: automatically classifying incoming requests, tickets, and cases • Prioritizing: applying priority levels to requests, tickets, and cases • Summarizing: condensing long meeting notes, transcripts, articles, and research • Obfuscating: identifying and anonymizing personal or sensitive data for GDPR and compliance • Translating: removing language barriers by translating content into any language • Organizing: grouping disparate data logically to simplify analysis • Analyzing: detecting sentiment, tone, and patterns such as SWOT groupings The common thread is efficiency: agents take on the repetitive, time-consuming parts of a task so people can focus on judgment calls and exceptions.
In Bizagi, AI agents draw on three main sources of data, combining internal knowledge, live external systems, and the live context of the process they're running in: • Enterprise Knowledge (internal data): A knowledge base that structures your company's information so agents can retrieve it instantly. • Live integrations (external data): Agents pull data in real time using Model Context Protocol (MCP), connecting to external systems such as CRMs to trigger actions and request data. • Process context (runtime data): When deployed inside a business process, agents use the active context of that workflow to decide what action is needed for the task at hand.
Combining these three sources means agents aren't operating on stale training data alone, they're grounded in what's happening in the business right now.
AI assistants resemble chatbots, as they provide answers when you directly ask them a question or instruct them to do something, such as summarizing information, drafting content, or explaining something.
Alternatively, as explained above, AI agents act on your behalf. You give them a goal, and they work out how to reach it by making decisions and taking actions autonomously. They work in the background without waiting to be asked and can trigger other systems, tools, and workflows to get on with the task at hand without you prompting them.
In practice, this means the two are used by different people for different reasons. An assistant like Bizagi's Ada helps a knowledge worker or manager understand their data and make a decision. An agent is embedded inside a process to make that process itself more efficient, for example, summarizing every incoming insurance claim so an agent doesn't need to read the full document. Bizagi offers both: Ada assists people within their apps, while AI agents increase efficiency inside business processes.
AI agents are designed to integrate with the systems, data, and workflows you already have in place. Inside a process, agents can: • Monitor for exceptions, triggers, or anomalies • Make or recommend decisions at critical steps • Automate repetitive tasks within a workflow • Escalate to a human when judgment is needed Crucially, you define where humans stay in the loop. You set the rules and decide which decisions require human sign-off, while the agent handles the rest , so adoption is incremental and governed, rather than all-or-nothing.
Traditional AI responds to a prompt. Agentic AI pursues a goal. Most AI tools wait for input and then produce an output; agentic AI is set a goal and works towards it, without needing to be prompted at every stage. Agentic AI can: • Set and pursue goals autonomously • Use tools, data, and systems to complete tasks • Adapt when circumstances change • Hand off to humans when judgment is needed
A useful analogy: a calculator (like a traditional AI assistant) waits for your input and returns an output. A financial analyst (agentic AI) takes your objective and works out how to get there. Our agentic automation blog takes a deeper dive on this topic if you would like to learn more.
Agentic AI features a range of abilities that enable AI systems to set goals, develop strategies to achieve them, and adapt with minimal human intervention. AI agents are real-world applications of this concept, built to operate within clearly defined boundaries.
In other words, every AI agent draws on agentic principles, but an individual agent is typically scoped to a specific task, with specific permissions and specific connectors, inside a specific step of a process. AI agents deploy logic safely and predictably in a real business, with the scope of autonomy defined upfront by the people who build and govern them.
You define what the agent can do; it decides how to do it. Without waiting for a human prompt, AI agents can gather data, connect to external systems, and trigger events, acting when the process needs them to, not only when someone asks. But autonomy doesn't mean unchecked. You can, and should: • Control which actions are available to each AI agent • Use existing or custom connectors for all actions • Require human checks in complex or unstructured scenarios
Agents are powerful because they act independently, but they're safe because you set the boundaries. Striking that balance, enough autonomy to drive real efficiency, enough governance to stay in control, is at the heart of how Bizagi approaches AI agents.
Find out how Bizagi's AI agents can boost productivity and drive efficiency across your business processes: Explore Bizagi AI Agents.
