Agentic AI offers two approaches to achieving automation: agentic workflows, which embed AI into predefined processes for much more predictable outcomes, and AI agents which autonomously plan, execute, and iterate towards a goal. Choosing between them depends on a number of different factors. Understanding when to embed a single LLM step into a deterministic process versus when to hand off decision-making to a goal-driven agent helps teams build more reliable, scalable, and adaptive systems.
Thus, this blog covers agentic workflows, how agents are different from workflows and when to use an agentic workflow.
What “agentic” really means
Agentic systems differ from traditional rule-based softwares in two key ways:
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- They leverage non-determinism powered by large language models, which allows the output to evolve across multiple runs rather than returning the same result each and every time.
- They exhibit agency, meaning that they can plan, choose, and sequence an action geared towards a goal rather than merrily executing a fixed set of instructions.
These traits cover both Agentic workflows which introduce AI steps into pre-defined pipelines and AI Agent which is a system powered by large language models (LLMs) that can take actions and act autonomously.
Agentic workflows: scoped AI within deterministic processes
When one hears the term “workflow,” one thinks of an orchestration of steps that always produces the same result given the same input.
Agentic workflows enhance existing software pipelines by inserting one or more llm-powered steps without affecting the overall predictability of that system. Workflows are systems where LLMs and tools are orchestrated through predefined code paths.
For example, an accounts system might: invoke a large language model to analyze a scanned invoice language, then it might use AI-driven data extraction to populate the structured fields and route the results for human review before the final submission is made to a processing tool. Thus, this sequence of actions remains linear and reproducible with AI enriching specific tasks under proper human oversight.
AI agents: autonomous, goal-driven orchestration
AI agents can run fully autonomously with a set of tools available to them in order to perform long running tasks. Rather than following a fixed pattern, the agent is given a goal, the resources, and agents can basically figure out the best route on its own. Agents can prioritize tasks, switch strategies, or even reflect on their own progress. AI agents have two jobs: they need to interact with people and then they must perform tasks. When talking to humans, AI agents can handle unstructured input, derive context, and explain concepts in human language. Taking the information from humans, AI agents then perform tasks for the human by calling APIs, learning from errors, and sometimes working with no human supervision. They orchestrate multiple steps to complete tasks in loops.
This flexibility is extremely powerful. Imagine a smart assistant that can understand requirements, generate some design ideas, fetch appropriate data from Couchbase, integrate with third-party APIs, and deliver an extremely custom experience all on the fly.
Choosing between AI agents and agentic workflows (and why it matters)
Autonomous AI agents are great for creative problem-solving but they do introduce a certain level of risk and predictability.
Thus, agent workflows are the sweet spot in order to add LLM-driven insights (such as summarization, intent matching where it matters) while still being able to keep control with deterministic steps for security and compliance purposes. One could also include human-in-the-loop processes for more mission-critical decisions. An example could be in voice processing with the help of an AI-powered extraction step, but the rest of the steps still remain in control of human intervention.
But meantime, fully autonomous agentic systems will excel in scenarios where autonomy outweighs the need for prediction, like doing autonomous research or providing hyper-personalized experiences. Thus AI agents are useful when you need autonomous problem solving across multiple steps and have the flexibility to adjust strategies.
For example, when creating a market research bot where the AI agent researches current trends in the data security market and prepares a strategic report. Here, the agent is able to learn from the news reports and social media, organize the findings in different sections, and finally summarize and suggest the next research step. A user can’t script every single source or insight, and this is where the agent’s autonomy brings a lot of value.
최종 생각
As we have understood, one should use agentic workflows when predictable results and control are extremely important and are being used for a more consistent and deterministic outcome. But you could leverage full-fledged AI agents when autonomy and creative problem-solving is required. One should keep in mind the risks and when to keep humans involved in order to avoid undesirable autonomous behavior.
Couchbase is the purpose-built data platform for scalable agentic AI applications, thanks to features like rich querying, real time analytics, vector search, agent catalog etc. Thus Couchbase offers the foundation you need to deploy your AI Agents.
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