Why Static Workflows Are Now Out of date

In a world formed by quickly rising expertise, companies and builders are regularly in search of smarter options that improve productiveness, personalization, and frictionless experiences. The inflow of recent agentic AI methods is reshaping how work is completed and the way duties are organized and accomplished. Static workflows, previously the guts of automation, are being changed by agentic architectures that study, adapt, and optimize work in actual time with no interplay or oversight. This weblog digs into the variations between the 2 AI paradigms, contains examples with code snippets, and explains why agentic methods are redefining and elevating the usual of automation.

What Are Static vs Agentic AI Programs?

Earlier than diving into the small print, let’s make clear what these phrases imply and why they matter.

Static AI Programs

A lot of these workflows are primarily based on inflexible, hardcoded sequences. They function linearly, with a inflexible set of sequences that overlook all about context or nuance: you present knowledge or set off occasions, and the system executes a pre-planned collection of operations. Basic examples embody rule-based chatbots, scheduled electronic mail reminders, and linear knowledge processing scripts.

Key Options of Static AI:

• Fastened logic: No deviations; each enter given yields the anticipated output.
• No personalization: work processes are the identical throughout all customers.
• No studying: missed alternatives are missed alternatives till you determine to reprogram.
• Low flexibility: If you need the right workflow, you’ll have to rewrite the code.

Agentic AI Programs

Agentic methods characterize a essentially new degree of autonomy. They draw inspiration from clever brokers (brokers) and may make selections, decide sub-goals, and revise actions primarily based on person suggestions, context, and understanding of their progress. Agentic AI methods do greater than carry out duties; they facilitate your complete course of, on the lookout for methods to reinforce the end result or course of.

Key Traits of Agentic AI:

• Adaptive logic: the flexibility to re-plan and adapt to a context
• Personalization: the flexibility to create distinctive experiences for every person and every state of affairs
• Studying-enabled: the flexibility to self-correct and incorporate suggestions to enhance
• Extremely versatile: the flexibility to allow new behaviors and optimizations with out human intervention.

Static vs. Agentic AI: Core Variations

Let’s summarize their variations in a  desk, so you possibly can rapidly grasp what units agentic AI aside

Palms On: Evaluating the Code

To showcase the useful variations, we’ll now stroll by way of the development of a Job Reminder Bot.

Instance 1: Static System Job Reminder Bot

This bot takes a activity and a deadline, places the reminder in place, and takes no motion after that. The person bears full duty for any updates; the bot can’t assist in any respect as soon as the deadline has been missed.

Code:

from datetime import datetime, timedelta

class AgenticBot:

    def __init__(self):

        self.reminders = {}

    def set_reminder(self, user_id, activity, deadline):

        self.reminders[user_id] = {

            'activity': activity,

            'deadline': deadline,

            'standing': 'pending'

        }

        return f"Agentic reminder: '{activity}', deadline is {deadline}."

    def update_status(self, user_id, standing):

        if user_id in self.reminders:

            self.reminders[user_id]['status'] = standing

            if standing == 'missed':

                self.suggest_reschedule(user_id)

    def suggest_reschedule(self, user_id):

        activity = self.reminders[user_id]['task']

        deadline_str = self.reminders[user_id]['deadline']

        strive:

            # For demo, faux "Friday" is 3 days later

            deadline_date = datetime.now() + timedelta(days=3)

            new_deadline = deadline_date.strftime("%A")

        besides Exception:

            new_deadline = "Subsequent Monday"

        print(f"Job '{activity}' was missed. Prompt new deadline: {new_deadline}")

    def proactive_check(self, user_id):

        if user_id in self.reminders:

            standing = self.reminders[user_id]['status']

            if standing == 'pending':

                print(f"Proactive verify: '{self.reminders[user_id]['task']}' nonetheless wants consideration by {self.reminders[user_id]['deadline']}.")

# Utilization

if __name__ == "__main__":

    bot = AgenticBot()

    print(bot.set_reminder("user1", "End report", "Friday"))

    # Simulate a missed deadline

    bot.update_status("user1", "missed")

    # Proactive verify earlier than deadline

    bot.proactive_check("user1")

Output:

Assessment:

• The script simply sends a affirmation that the motion is full.
• No follow-up after the duty of placing it in place if the deadline was missed.
• If deadlines change or duties change, the person should act on the knowledge manually. 

Instance 2: Agentic Job Reminder Bot

This bot is far more clever. It tracks progress, takes initiative to verify in, and suggests options if timelines stray.

Code:

from datetime import datetime, timedelta

class TrulyAgenticBot:

    def __init__(self):

        self.duties = {}  # user_id -> activity data

    def decompose_goal(self, objective):

        """

        Simulated reasoning that decomposes a objective into subtasks.

        This mimics the pondering/planning of an agentic AI.

        """

        print(f"Decomposing objective: '{objective}' into subtasks.")

        if "report" in objective.decrease():

            return [

                "Research topic",

                "Outline report",

                "Write draft",

                "Review draft",

                "Finalize and submit"

            ]

        else:

            return ["Step 1", "Step 2", "Step 3"]

    def set_goal(self, user_id, objective, deadline_days):

        subtasks = self.decompose_goal(objective)

        deadline_date = datetime.now() + timedelta(days=deadline_days)

        self.duties[user_id] = {

            "objective": objective,

            "subtasks": subtasks,

            "accomplished": [],

            "deadline": deadline_date,

            "standing": "pending"

        }

        print(f"Purpose set for person '{user_id}': '{objective}' with {len(subtasks)} subtasks, deadline {deadline_date.strftime('%Y-%m-%d')}")

    def complete_subtask(self, user_id, subtask):

        if user_id not in self.duties:

            print(f"No lively duties for person '{user_id}'.")

            return

        task_info = self.duties[user_id]

        if subtask in task_info["subtasks"]:

            task_info["subtasks"].take away(subtask)

            task_info["completed"].append(subtask)

            print(f"Subtask '{subtask}' accomplished.")

            self.reflect_and_adapt(user_id)

        else:

            print(f"Subtask '{subtask}' not in pending subtasks.")

    def reflect_and_adapt(self, user_id):

        """

        Agentic self-reflection: verify subtasks and modify plans.

        For instance, add an additional evaluate if the draft is accomplished.

        """

        activity = self.duties[user_id]

        if len(activity["subtasks"]) == 0:

            activity["status"] = "accomplished"

            print(f"Purpose '{activity['goal']}' accomplished efficiently.")

        else:

            # Instance adaptation: if draft achieved however no evaluate, add "Additional evaluate" subtask

            if "Write draft" in activity["completed"] and "Assessment draft" not in activity["subtasks"] + activity["completed"]:

                print("Reflecting: including 'Additional evaluate' subtask for higher high quality.")

                activity["subtasks"].append("Additional evaluate")

            print(f"{len(activity['subtasks'])} subtasks stay for objective '{activity['goal']}'.")

    def proactive_reminder(self, user_id):

        if user_id not in self.duties:

            print("No duties discovered.")

            return

        activity = self.duties[user_id]

        if activity["status"] == "accomplished":

            print(f"Person '{user_id}' activity is full, no reminders wanted.")

            return

        days_left = (activity["deadline"] - datetime.now()).days

        print(f"Reminder for person '{user_id}': {days_left} day(s) left to finish the objective '{activity['goal']}'")

        print(f"Pending subtasks: {activity['subtasks']}")

        if days_left 

Output:

Assessment:

This script exhibits what makes a system agentic. Not like the static bot, it doesn’t simply set reminders; it breaks a objective into smaller items, adapts when circumstances change, and proactively nudges the person. The bot displays on progress (including additional evaluate steps when wanted), retains monitor of subtasks, and even suggests rescheduling deadlines as a substitute of ready for human enter.

It demonstrates autonomy, context-awareness, and flexibility — the hallmarks of an agentic system. Even with out LLM integration, the design illustrates how workflows can evolve in actual time, recuperate from missed steps, and modify themselves to enhance outcomes

Subsequently, even within the absence of an LLM functionality, that system demonstrates the core ideas of agentic AI if it will probably reveal all these capabilities.

• Versatile Job Decomposition: Crumbles advanced objectives into subtasks to make use of a extra autonomous strategy to planning quite than a predetermined script.
• Energetic Standing Monitoring: Retains monitor of each accomplished and unfinished duties to offer well timed, context-aware updates.
• Self-Reflection and Means to Change: Modify workflow by including subtasks when crucial, demonstrating a discovered capability.
• Proactive Reminders/Rescheduling: Sends a reminder (consciousness to the extent of urgency) and suggests altering deadlines if crucial mechanically.
• Usually Versatile and Autonomous: Function independently with the flexibility to adapt in actual time with out handbook change.
• Academic, but Actual-World: Demonstrates the ideas of agentic AI, even with out integration with different types of LLM.

What are the Causes Static Workflows are Unhealthy in an Group? 

As enterprise necessities evolve towards flexibility, automation, and personalization, we are able to now not work with static workflows:

• Inefficient: It requires somebody to intervene for it to alter.
• Topic to human error: It requires specific coding each time it modifications, or somebody to make a change.
• No consciousness/studying: The system can’t turn out to be “smarter” over time.

Agentic AI methods can:

• Study from person actions: They will tackle failures and context shifts, or re-plan their actions all through a workflow. 
• Present a proactive expertise: lowering busywork and rising person expertise.
• Present accelerated productiveness by decreasing the complexity of workflows with minimal supervision.

The place would possibly you apply agentic approaches?

Agentic workflows are helpful in all places, the place adaptability, personalization, and steady enhancements drive higher outcomes.

• Buyer Service: Brokers who decide when/how the difficulty is resolved and solely escalate to people when applicable.
• Challenge Administration: Brokers that may reschedule and alter the calendar primarily based on precedence modifications.
• Gross sales Automation: Brokers that may adapt and alter the outreach technique primarily based on buyer suggestions and habits.
• Well being Monitoring:  Brokers that may change notifications or suggestions primarily based on affected person progress.

Conclusion

The shift from static AI to agentic AI methods has opened a brand new chapter for what automation can do. With autonomous workflows, the necessity for fixed supervision has been eliminated, permitting workflows to behave inside their frames of motion in accordance with particular person wants and altering circumstances. With the assist of agentic architectures, organizations and builders are capable of make their organizations extra future-proof and supply considerably higher experiences for his or her customers, making the previous paradigm of static workflows out of date.

Ceaselessly Requested Questions

Knowledge Scientist | AWS Licensed Options Architect | AI & ML Innovator

As a Knowledge Scientist at Analytics Vidhya, I focus on Machine Studying, Deep Studying, and AI-driven options, leveraging NLP, pc imaginative and prescient, and cloud applied sciences to construct scalable purposes.

With a B.Tech in Laptop Science (Knowledge Science) from VIT and certifications like AWS Licensed Options Architect and TensorFlow, my work spans Generative AI, Anomaly Detection, Faux Information Detection, and Emotion Recognition. Keen about innovation, I attempt to develop clever methods that form the way forward for AI.