Why Static Workflows Are Now Out of date

In a world formed by quickly rising expertise, companies and builders are frequently looking for smarter options that improve productiveness, personalization, and frictionless experiences. The inflow of latest agentic AI techniques is reshaping how work is completed and the way duties are organized and accomplished. Static workflows, previously the center 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, consists of examples with code snippets, and explains why agentic techniques are redefining and elevating the usual of automation.

What Are Static vs Agentic AI Methods?

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

Static AI Methods

These kinds of 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 information or set off occasions, and the system executes a pre-planned collection of operations. Traditional examples embrace rule-based chatbots, scheduled electronic mail reminders, and linear information processing scripts.

Key Options of Static AI:

• Mounted 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 Methods

Agentic techniques characterize a essentially new degree of autonomy. They draw inspiration from clever brokers (brokers) and might make decisions, decide sub-goals, and revise actions primarily based on person suggestions, context, and understanding of their progress. Agentic AI techniques do greater than carry out duties; they facilitate your complete course of, searching for methods to boost 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 scenario
• 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 shortly grasp what units agentic AI aside

Palms On: Evaluating the Code

To showcase the useful variations, we are going to now stroll via the development of a Activity Reminder Bot.

Instance 1: Static System Activity Reminder Bot

This bot takes a job and a deadline, places the reminder in place, and takes no motion after that. The person bears full accountability 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, job, deadline):

        self.reminders[user_id] = {

            'job': job,

            'deadline': deadline,

            'standing': 'pending'

        }

        return f"Agentic reminder: '{job}', 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):

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

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

        strive:

            # For demo, fake "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"Activity '{job}' was missed. Recommended 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 examine: '{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 examine 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 Activity Reminder Bot

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

Code:

from datetime import datetime, timedelta

class TrulyAgenticBot:

    def __init__(self):

        self.duties = {}  # user_id -> job information

    def decompose_goal(self, objective):

        """

        Simulated reasoning that decomposes a objective into subtasks.

        This mimics the considering/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"Aim 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 energetic 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: examine subtasks and alter plans.

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

        """

        job = self.duties[user_id]

        if len(job["subtasks"]) == 0:

            job["status"] = "accomplished"

            print(f"Aim '{job['goal']}' accomplished efficiently.")

        else:

            # Instance adaptation: if draft carried out however no evaluation, add "Additional evaluation" subtask

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

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

                job["subtasks"].append("Additional evaluation")

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

    def proactive_reminder(self, user_id):

        if user_id not in self.duties:

            print("No duties discovered.")

            return

        job = self.duties[user_id]

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

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

            return

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

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

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

        if days_left <= 1:

            print("⚠️ Pressing: Deadline approaching!")

    def suggest_reschedule(self, user_id, extra_days=3):

        """

        Robotically suggests rescheduling if the duty is overdue or wants extra time.

        """

        job = self.duties.get(user_id)

        if not job:

            print("No job discovered to reschedule.")

            return

        new_deadline = job["deadline"] + timedelta(days=extra_days)

        print(f"Suggesting new deadline for '{job['goal']}': {new_deadline.strftime('%Y-%m-%d')}")

        job["deadline"] = new_deadline

# Demo utilization to match in your weblog:

if __name__ == "__main__":

    agentic_bot = TrulyAgenticBot()

    # Step 1: Set person objective with deadline in 5 days

    agentic_bot.set_goal("user1", "End quarterly report", 5)

    # Step 2: Full subtasks iteratively

    agentic_bot.complete_subtask("user1", "Analysis subject")

    agentic_bot.complete_subtask("user1", "Define report")

    # Step 3: Proactive reminder earlier than deadline

    agentic_bot.proactive_reminder("user1")

    # Step 4: Full extra subtasks

    agentic_bot.complete_subtask("user1", "Write draft")

    # Step 5: Mirror provides an additional evaluation subtask

    agentic_bot.complete_subtask("user1", "Assessment draft")

    # Step 6: Full added subtask

    agentic_bot.complete_subtask("user1", "Additional evaluation")

    agentic_bot.complete_subtask("user1", "Finalize and submit")

    # Step 7: Remaining proactive reminder (job ought to be accomplished)

    agentic_bot.proactive_reminder("user1")

    # Bonus: Recommend rescheduling if person wanted further time

    agentic_bot.suggest_reschedule("user1", extra_days=2)

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 further evaluation 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, get better from missed steps, and alter themselves to enhance outcomes

Due to this fact, even within the absence of an LLM functionality, that system demonstrates the core ideas of agentic AI if it could possibly exhibit all these capabilities.

• Versatile Activity Decomposition: Crumbles advanced targets into subtasks to make use of a extra autonomous method to planning quite than a predetermined script.
• Energetic Standing Monitoring: Retains monitor of each accomplished and unfinished duties to supply well timed, context-aware updates.
• Self-Reflection and Capability to Change: Modify workflow by including subtasks when vital, demonstrating a discovered capability.
• Proactive Reminders/Rescheduling: Sends a reminder (consciousness to the extent of urgency) and suggests altering deadlines if vital routinely.
• Usually Versatile and Autonomous: Function independently with the flexibility to adapt in actual time with out handbook change.
• Instructional, 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 will 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 adjustments, or somebody to make a change.
• No consciousness/studying: The system can’t grow to be “smarter” over time.

Agentic AI techniques can:

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

The place may you apply agentic approaches?

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

• Buyer Service: Brokers who decide when/how the problem is resolved and solely escalate to people when acceptable.
• Mission Administration: Brokers that can reschedule and alter the calendar primarily based on precedence adjustments.
• Gross sales Automation: Brokers that can adapt and alter the outreach technique primarily based on buyer suggestions and conduct.
• Well being Monitoring:  Brokers that can change notifications or suggestions primarily based on affected person progress.

Conclusion

The shift from static AI to agentic AI techniques 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 based on particular person wants and altering circumstances. With the help of agentic architectures, organizations and builders are in a position to 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 functions.

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 techniques that form the way forward for AI.