4 min read
Agentic Workflows: Moving Beyond Chatbots to Autonomous Task Execution
The chatbot era is ending. In 2025, we’re moving from AI that answers questions to AI that completes tasks. This is the shift from conversational AI to agentic workflows.
What Are Agentic Workflows?
Agentic workflows are AI systems that:
- Receive high-level goals (not step-by-step instructions)
- Plan their own approach
- Execute actions autonomously
- Adapt when things go wrong
- Complete tasks end-to-end
# Old way: Chatbot
user: "How do I create a sales report?"
ai: "Here are the steps: 1. Query the database... 2. Create a pivot table..."
# New way: Agentic workflow
user: "Create a Q4 sales report for the executive team"
ai: *queries database, analyzes data, creates visualizations, generates report, sends to team*
ai: "Done. I've created the Q4 sales report and sent it to the executive distribution list. Here's a summary..."Anatomy of an Agentic Workflow
from azure.ai.foundry.workflows import AgenticWorkflow, Goal, Action
# Define the workflow
sales_report_workflow = AgenticWorkflow(
name="SalesReportWorkflow",
goal=Goal(
description="Generate comprehensive sales reports",
success_criteria=[
"Report contains accurate data",
"Visualizations are clear and relevant",
"Executive summary is concise",
"Report is delivered to stakeholders"
]
),
available_actions=[
Action("query_sales_data", query_sales_database),
Action("analyze_trends", run_trend_analysis),
Action("create_visualizations", generate_charts),
Action("write_summary", generate_executive_summary),
Action("format_report", create_pdf_report),
Action("send_report", email_to_distribution_list)
],
planning_model="gpt-4o",
execution_model="gpt-4o-mini", # Cheaper for routine steps
max_iterations=20
)
# Execute with minimal input
result = await sales_report_workflow.execute(
input={
"time_period": "Q4 2024",
"audience": "executive_team",
"focus_areas": ["revenue", "growth", "regional_performance"]
}
)The Planning-Execution Loop
Agentic workflows follow a continuous loop:
from azure.ai.foundry.workflows import Planner, Executor, Monitor
class AgenticLoop:
def __init__(self, goal, actions, models):
self.planner = Planner(model=models["planning"])
self.executor = Executor(model=models["execution"])
self.monitor = Monitor()
self.goal = goal
self.actions = actions
async def run(self, input_data):
state = {"input": input_data, "completed_steps": [], "observations": []}
while not self.goal.is_achieved(state):
# Plan next steps
plan = await self.planner.create_plan(
goal=self.goal,
current_state=state,
available_actions=self.actions
)
# Execute planned actions
for step in plan.next_steps:
try:
result = await self.executor.execute(step, state)
state["completed_steps"].append(step)
state["observations"].append(result)
except Exception as e:
# Adapt to failures
state["observations"].append(f"Failed: {e}")
# Planner will adjust on next iteration
# Monitor progress
self.monitor.log_iteration(state)
if self.monitor.detect_loop():
raise WorkflowStuckError("No progress detected")
return stateReal-World Agentic Workflow Examples
Data Pipeline Incident Response
incident_workflow = AgenticWorkflow(
name="PipelineIncidentResponse",
goal=Goal(
description="Resolve data pipeline incidents with minimal downtime",
success_criteria=[
"Root cause identified",
"Pipeline restored to healthy state",
"Incident documented",
"Stakeholders notified"
]
),
available_actions=[
Action("get_pipeline_status", check_pipeline_health),
Action("query_logs", search_error_logs),
Action("check_dependencies", verify_upstream_systems),
Action("restart_job", restart_failed_job),
Action("scale_resources", adjust_compute_capacity),
Action("rollback_changes", revert_recent_deployment),
Action("create_incident", open_incident_ticket),
Action("notify_oncall", page_oncall_engineer),
Action("update_status", post_status_update)
],
autonomy_config={
"auto_approve": ["get_pipeline_status", "query_logs", "check_dependencies"],
"require_approval": ["rollback_changes", "notify_oncall"],
"always_execute": ["create_incident", "update_status"]
}
)Customer Data Request Fulfillment
data_request_workflow = AgenticWorkflow(
name="CustomerDataRequest",
goal=Goal(
description="Fulfill customer data requests while ensuring compliance",
success_criteria=[
"Request validated against policy",
"Data extracted accurately",
"PII properly handled",
"Audit trail created",
"Data delivered securely"
]
),
available_actions=[
Action("validate_request", check_request_compliance),
Action("verify_authorization", check_user_permissions),
Action("extract_data", query_customer_data),
Action("mask_pii", apply_pii_masking),
Action("generate_audit", create_audit_record),
Action("package_data", create_secure_export),
Action("deliver_data", send_via_secure_channel)
],
guardrails=[
"Never expose raw PII without masking",
"Always create audit records",
"Verify authorization before data access"
]
)Building Reliable Agentic Workflows
Checkpointing and Recovery
from azure.ai.foundry.workflows import Checkpoint
workflow = AgenticWorkflow(
name="LongRunningAnalysis",
checkpoint_config=Checkpoint(
backend="blob_storage",
frequency="after_each_action",
retention_hours=72
)
)
# If workflow fails, resume from checkpoint
result = await workflow.execute(
input=analysis_request,
resume_from_checkpoint=True # Continues from last successful step
)Human-in-the-Loop
from azure.ai.foundry.workflows import HumanApproval
workflow = AgenticWorkflow(
name="FinancialReport",
human_checkpoints=[
HumanApproval(
before_action="publish_report",
approvers=["finance_team"],
timeout_minutes=60,
timeout_action="escalate"
),
HumanApproval(
condition="cost_exceeds_threshold",
threshold={"estimated_cost": 100},
approvers=["manager"]
)
]
)Guardrails and Boundaries
from azure.ai.foundry.workflows import Guardrail
workflow = AgenticWorkflow(
name="DataModification",
guardrails=[
Guardrail(
name="no_production_writes",
rule="Never write to production databases without explicit approval",
enforcement="block"
),
Guardrail(
name="cost_limit",
rule="Total workflow cost must not exceed $50",
enforcement="stop_and_alert"
),
Guardrail(
name="time_limit",
rule="Workflow must complete within 2 hours",
enforcement="timeout"
)
]
)Monitoring Agentic Workflows
from azure.ai.foundry.workflows import WorkflowMonitor
monitor = WorkflowMonitor(
backend="azure_monitor",
dashboards=True
)
# Track workflow metrics
@monitor.track
async def run_workflow(input):
return await workflow.execute(input)
# Metrics captured:
# - Total execution time
# - Steps completed vs planned
# - Replanning events
# - Human intervention rate
# - Success/failure rate
# - Cost per workflowThe Future is Agentic
Agentic workflows represent a fundamental shift in how we build AI systems:
| Chatbots | Agentic Workflows |
|---|---|
| Answer questions | Complete tasks |
| Single turn | Multi-step |
| User drives | AI drives |
| Advisory | Autonomous |
| Simple | Complex |
Start small: identify repetitive, well-defined tasks in your organization. Build agentic workflows for those first. As you gain confidence, expand to more complex scenarios.
The organizations that master agentic workflows in 2025 will have a significant competitive advantage. The time to start is now.