With artificial intelligence transforming from reactive systems to autonomous agents, we are poised at the threshold of a new technological frontier—Agentic AI. Such AI systems are capable of seeking long-term objectives, autonomously making decisions, and engaging with digital or real-world ecosystems in a more independent fashion.

While this holds out the promise of leaps in automation, productivity, and personalization, the journey to creating truly helpful and secure agentic AI is filled with obstacles—technical, ethical, and operational.

In this blog, we discuss the main challenges organizations encounter when dealing with agentic AI and what it means to business for embedding intelligence more deeply in their ecosystems.

What is Agentic AI

Agentic AI refers to AI that can act on its own to reach objectives. These types of systems have the following characteristics:

• Goal-setting and planning: They are able to decompose goals into steps or subgoals.

• Autonomous decision-making: They are able to make decisions without human input, adapting with feedback.

• Persistent behavior: They have memory or state over sessions.

• Tool use: They are able to deal with APIs, software tools, or robotic actuators.

In contrast to conventional AI, which reacts to inputs or prompts, agentic AI is independent, frequently generating missions, devising strategies, or working with other systems to achieve multifaceted goals.

1. Control and Alignment

The most critical challenge is keeping agentic AI systems aligned with human purpose. As agents develop greater ability, the threat increases that they:

• Misunderstand vague objectives.

• Chase results in manners that contradict ethical or legal constraints.

• Optimize for metrics in unforeseen ways (reward hacking).

A customer service agentic AI might maximize quickly closing tickets through providing generic or unhelpful responses—meeting the KPI at the expense of customer experience.

Mitigation Strategies:

• Reinforcement learning with human feedback (RLHF)

• Multi-objective optimization with safety constraints

• Simulation and stress testing for emergent behavior

2. Complexity of Multi-step Reasoning

Agentic AI tends to require multi-step planning, e.g.,

• Booking travel across several platforms

• Troubleshooting an IT issue through iterative diagnostics

• Coordinating a marketing campaign workflow

Every step adds branching logic, dependencies, and points of failure. Language models such as GPT-4 are great at reasoning in isolation within tasks, but chaining those steps reliably and securely is still under development.

Challenge: Coherence, context, and continuity over longer lifecycles of agents.

Mitigation:

• Memory management systems (episodic, semantic)

• Workflow orchestration frameworks such as LangChain, CrewAI

• Hierarchical agents (planners + executors)

3. Environmental Uncertainty and Tool Integration

Agentic systems communicate with APIs, databases, cloud services, IoT devices, and occasionally physical worlds. These worlds are, however, not:

• Static (APIs change, systems crash)

• Complete (incomplete or incorrect data)

• Free (permissions, rate limits, latency)

An agent needs to recover with style from failures, retries, out-of-date documentation, or misbehaving tools. This brings a large engineering overhead.

Mitigation:

• Observability stacks (logging, tracing, auditing)

• Guardrails and fallback plans

• Test harness with simulated worlds

4. Security and Abuse Risk

Autonomous agents can be weaponized. If misconfigured or exploited, they can:

• Exfiltrate data

• Conduct phishing campaigns

• Overuse cloud resources

• Bypass access controls

Because agents operate autonomously, malicious prompt injection, code injection, or supply-chain attacks can have disproportionate impact.

Enterprise Risks:

• Data leakage

• Regulatory non-compliance (GDPR, HIPAA, etc.)

• Financial or reputational loss

Mitigation:

• Least privilege principle

• Prompt sanitization and context isolation

• Usage monitoring and kill switches

5. Ethical and Legal Ambiguities

Agentic AI poses new questions in AI ethics and governance:

• Who is responsible if a self-driving agent injures someone?

• How do we make its decision-making transparent?

• Can agents be relied upon for confidential or high-consequence decisions?

In fields such as finance, healthcare, and law, this isn’t theoretical—it’s a matter of survival.

Future Regulatory Themes:

• Auditability of autonomous systems

• Liability frameworks for AI

• Human-in-the-loop requirements for high-stakes domains

6. Evaluation and Benchmarking

Traditional ML models generate a straightforward output. Agentic systems, however, take actions, trace trajectories, and change over time.

Evaluating them is therefore non-trivial. Metrics must capture:

• Task success rate

• Safety violations

• Resource usage

• User satisfaction

Yet, there are no widely accepted benchmarks for agentic AI as of today. This makes R&D efforts hard to compare and enterprise rollouts difficult to validate.Conclusion: Proceed, But Proceed With Guardrails

Agentic AI is an exciting but double-edged innovation. For enterprises, it unlocks potential in intelligent automation, 24/7 digital agents, and proactive customer engagement.

Yet, scaling up such systems involves a sophisticated design, testing, governance, and ethics approach. It’s not merely creating better agents—it’s making sure they work towards our objectives, safely and reliably.

At Verbat, we remain at the cutting edge of new AI innovations. Whether you’re investigating digital agents in customer service, business automation, or intelligent applications, we can assist in designing solutions that weigh innovation against responsibility.