Main Facts in Brief:

• Most “agentic AI” systems are enhanced workflows with natural language triggers, not true autonomous agents
• Payment infrastructure emerged as the first practical application, with companies like Skyfire processing procurement transactions autonomously
• Healthcare agents achieve safety parity with trained nurses for non-diagnostic tasks like medication reminders
• Agentic AI in customer service automation handles 80% of inquiries by 2029 according to industry predictions
• Supply chain optimization shows measurable ROI, with companies like Walmart using AI for inventory management
• Security testing accelerated from 14 days to 1 hour while catching 20% more issues than manual audits
• Most Agentic AI deployments remain constrained to narrow use cases due to debugging challenges and safety concerns

What Agentic AI Actually Means

Agentic AI systems can independently assess problems, plan action sequences, execute those plans across multiple tools, and learn from outcomes without constant human oversight. Unlike traditional automation that follows predetermined scripts, these systems write their own workflows based on goals and adapt their approach based on results.

The reality behind the buzzword reveals a more nuanced picture. Most current implementations blend natural language interfaces with structured workflows rather than achieving true autonomy. Companies deploy these systems primarily for high-volume, predictable tasks where speed and consistency matter more than creative problem-solving.

Real Agentic AI Workflows Companies Deploy Today

Travel Booking and Expense Management

Travel agents handle complex multi-step processes: checking corporate policies, comparing flight options, booking accommodations, and updating expense systems. Modern implementations use natural language processing to understand travel requests, then execute structured workflows across multiple booking platforms.

The system maintains context throughout the process, remembering preferences like aisle seats or hotel chains while ensuring compliance with corporate travel policies. Integration challenges typically involve authentication across multiple vendor APIs and handling edge cases like flight cancellations.

Inbox Triage and Email Automation

Email management systems categorize incoming messages, draft responses based on content analysis, and route complex issues to appropriate team members. One implementation automatically compiled security update summaries overnight, reducing manual work from hours to minutes.

These systems excel at pattern recognition within email content but struggle with nuanced communication requiring emotional intelligence. Human oversight remains essential for sensitive customer communications or internal policy discussions.

Research and Information Synthesis

Research agents gather information from multiple sources, synthesize findings, and generate comprehensive reports. AI Scientist-v2 represents an advanced example, autonomously generating hypotheses, designing experiments, and producing peer-reviewed research papers (Reference 5). This system explores multiple experimental pathways simultaneously, scoring hypotheses for novelty and plausibility.

Causaly operates a biomedical research platform that autonomously plans research workflows using a 500 million-fact knowledge graph, prioritizing evidence and maintaining source transparency .

Procurement and Supply Cain Optimization

Procurement systems monitor inventory levels, evaluate supplier performance, and automatically place orders when stock reaches predetermined thresholds. Walmart’s implementation watches inventory patterns and reorders products before shortages occur. DHL uses similar systems to optimize delivery routes based on real-time traffic data.

These systems demonstrate clear ROI through reduced carrying costs and improved service levels. However, they require careful configuration to handle supplier relationship management and quality control processes.

Customer Service Automation

Support systems analyze incoming requests, access customer histories, check policy databases, and generate personalized responses. One implementation reads customer emails, pulls account information, checks billing policies, and either resolves issues automatically or prepares complete context for human agents.

The key success factor involves defining clear escalation triggers for complex situations requiring human judgment or empathy.

Financial Transaction Processing

Payment infrastructure specifically designed for autonomous agents enables real-world financial transactions. Skyfire issues wallet IDs funded through traditional methods, allowing enterprises to control agent spending through real-time dashboards. Denso used this system for automated procurement payments, completing supply chain transactions without human intervention.

Payman bridges agents into traditional banking systems, enabling scenarios where AI agents hire freelancers, monitor task completion, and release payments automatically.

Cybersecurity and Threat Detection

Security systems continuously probe enterprise environments, identifying vulnerabilities and prioritizing them by exploitability and business risk. Terra Security deploys specialized agent fleets that focus on specific exploit types like SQL injection or privilege escalation.

AES reduced safety audit time from 14 days to 1 hour while catching 20% more issues than manual processes. The system costs 99% less than traditional auditing approaches.

Healthcare Patient Monitoring

Healthcare agents handle non-diagnostic tasks including post-discharge follow-ups, chronic disease coaching, and medication reminders. Hippocratic AI trains agents that achieved safety parity with trained nurses through clinical trials involving over 5,000 healthcare professionals.

These systems formed partnerships with major health systems like Memorial Hermann and Universal Health Services by late 2024.

Current Implementation Architecture

Component	Function	Common Tools
Planning Engine	Breaks complex goals into executable steps	GPT-4, Claude, Custom LLMs
Tool Integration	Connects to external systems and APIs	Zapier, Microsoft Power Automate, Custom connectors
Memory System	Maintains context and learns from outcomes	Vector databases, Redis, Custom state management
Safety Controls	Monitors actions and enforces constraints	Rule engines, Human-in-loop systems, Audit trails
Feedback Loop	Evaluates performance and adjusts behavior	Analytics platforms, A/B testing, Performance metrics

Platform Comparison

Platform	Best For	Strengths	Limitations
Relevance AI	Smart search and recommendations	Strong developer tools, customizable	Requires technical expertise
Microsoft Autogen	Teams using Microsoft products	Native integration, collaborative agents	Limited outside Microsoft ecosystem
CrewAI	Multi-agent coordination	Team management features	Complex setup
IBM Watsonx	Enterprise compliance	Governance controls, security	High cost, complex implementation
Cognosys	Autonomous decision-making	Minimal oversight required	Less transparency in decision process

Common Pitfalls and Debugging Challenges

The Black Box Problem

Debugging agentic systems resembles “reasoning with a hallucinating intern who forgets what they just did”. Traditional software debugging tools fail because these systems don’t follow predictable execution paths. Even with trace logs and prompt chains, understanding why specific decisions occurred remains difficult.

State Management and Recovery

Agents struggle to maintain consistent state across complex workflows and recover gracefully from failures. Unlike humans who adapt on the fly, systems break entirely when encountering unexpected API responses or authentication errors.

Tool Integration Fragility

API name mismatches, authentication failures, or schema changes can break entire workflows. Systems lack the contextual understanding to adapt when external services change their interfaces.

Over-Reliance and Silent Failures

Agents can “quietly go off-track” when misinterpreting priorities, with problems discovered only during periodic reviews. This creates risks where automated systems make consequential decisions based on incorrect assumptions.

Performance Evaluation Challenges

Outside narrow task benchmarks, measuring whether these Agentic AI systems perform well or “just get lucky” remains difficult. Organizations lack standardized metrics for evaluating autonomous decision-making quality.

Risk Mitigation Strategies Related to the Use of Agentic AI

Risk Category	Mitigation Approach	Implementation Example
Decision Errors	Human-in-the-loop checkpoints	Require approval for transactions over $10,000
System Failures	Graceful degradation paths	Fallback to manual processes when APIs fail
Security Issues	Strict access controls	Separate agent credentials with limited permissions
Compliance Violations	Automated audit trails	Log all decisions with reasoning and data sources
Performance Drift	Continuous monitoring	Weekly performance reviews with adjustment protocols

Agentic AI: 10-Step Starter Checklist

1. Identify High-Volume, Predictable Processes – Focus on tasks with clear inputs, outputs, and success criteria rather than creative or strategic work.
2. Map Current Workflow Dependencies – Document every system, API, and human touchpoint in your target process before attempting automation.
3. Define Success Metrics and Failure Conditions – Establish measurable criteria for performance and clear triggers for human intervention.
4. Choose Platform Based on Technical Resources – Match Agentic AI platform complexity to your team’s development capabilities and integration requirements.
5. Start with Read-Only Access – Begin Agentic AI implementations that gather and analyze information before granting systems the ability to take actions.
6. Build Comprehensive Logging Systems – Implement detailed audit trails that capture decisions, data sources, and reasoning paths for debugging.
7. Design Human Override Mechanisms – Create simple ways for users to interrupt, redirect, or reverse automated actions performed by Agentic AI when needed.
8. Test in Isolated Environments – Use sandbox systems that mirror production but can’t impact real business processes during development.
9. Implement Gradual Rollout Strategies – Deploy to small user groups first, gathering feedback before expanding Agentic AI to full organizational use.
10. Establish Regular Review Cycles – Schedule weekly performance assessments to identify drift, errors, or opportunities for improvement.

The Reality Behind the Hype

Current Agentic AI deployments primarily represent “scripted workflows in the backend, usually by a RAG framework” rather than truly autonomous systems. When scope remains narrow and implementation quality high, these systems can appear to have genuine agency, but most operate within tightly constrained parameters.

The main advancement involves natural language triggering workflows instead of code or UI buttons, essentially representing “the next step after low-code and no-code tools”. Companies claiming truly agentic AI typically work within very constrained use cases or engage in marketing exaggeration.

Industry experts suggest these systems resemble “rebranded expert systems from the ’80s, now with a natural language interface layered on top”. The backend remains “a brittle web of conditional logic that doesn’t generalize” beyond specific programmed scenarios.

Future Outlook and What Should You Consider?

The Agentic AI technology continues evolving rapidly, with improvements in context understanding, multi-agent collaboration, and adaptability. Companies implementing agentic workflows now gain advantages in speed, efficiency, and talent attraction as employees prefer working on strategic problems rather than repetitive tasks.

However, fundamental limitations around debugging, evaluation, and generalization suggest that breakthrough applications will continue focusing on narrow, well-defined domains rather than general-purpose automation. Organizations should approach implementation with realistic expectations while remaining prepared for rapid technological advancement.

In general, real-world applications increasingly reward companies that deploy AI strategically across core business functions, making early experimentation valuable despite current limitations. Success requires balancing automation benefits with human oversight, maintaining flexibility as capabilities expand.

If you are interested in this topic, we suggest you check our articles:

• Which LLM is the Best for Answering User Queries?
• Large Language Models (LLMs): The Basics Explained
• Open Source vs Proprietary LLMs: The Key Differences

Sources: Reddit, TechRepublic, David Linthicum @ LinkedIn, Merantix Capital, IBM, Superhuman

Written by Alius Noreika