Agentic AI: Navigating the Critical Issues and Transformative Opportunities

July 3, 2025 · 8 min read

Founder @ Simtel.AI

By Ravi Kaushik, Founder – Simtel.ai
📅 July 3, 2025
📍 Bangalore, India

Bangalore, July 2025 — A recently discovered flaw in Microsoft’s Copilot revealed that data from private GitHub repositories—once briefly public—remained accessible via cached AI responses. This echoes growing concerns in developer communities: AI-generated code may look correct, but fail quietly and dangerously in production.

Behind the excitement surrounding GitHub Copilot, Claude, and autonomous code agents lies a less-discussed truth: agentic AI coding is still in its infancy—brilliant in demos, brittle in deployment.

The Story of the Forgotten Code Block

Once upon a sprint, a small team of developers quickly scaffolded an automation flow using their favorite low-code tool. The first version was beautiful—clean modules, well-labeled nodes, and documentation inline.

But as product requirements shifted, more logic was wedged into the same workflow. A quick fix here, a duplicated condition there. By the third sprint, functions had been pasted multiple times with slight variations. Each new teammate added their own flavor—some used callbacks, others async/await. API tokens were hardcoded in one block, loaded from .env in another.

Soon, the original logic was buried under layers of "temporary" patches. Modules had overlapping responsibilities. Naming conventions diverged. No one dared touch the oldest nodes, afraid they'd collapse the fragile domino tower.

At one point, a junior developer cloned a workflow to create a variant—but forgot to update all endpoints. Two inconsistent versions were now in production. Bugs surfaced. Nobody could tell which version was correct, or which block still mattered.

Eventually, the team paused all feature work for a full sprint just to "clean up the mess."

The moral?
Code rot is real—even in visual or modular systems. Without structure, governance, and visibility, your "quick wins" become technical debt. Reusable, composable architecture matters—not just the first time you build, but every time you iterate.

When Agentic Coding Becomes Expensive Technical Debt

It started with a small task—"Just ask Claude to generate a webhook handler." The response was instant, and the developer pasted it straight into the project. A quick test, it worked. Everyone was impressed.

But soon, every feature was delegated to the AI.

Need a cron job? Ask Claude.
Want an auth middleware? Ask Claude.
Need an integration with a third-party API? Ask Claude again.

Each time, the code was slightly different. Sometimes verbose, sometimes abstract. Functions were duplicated across files. Similar logic was rewritten with different libraries. The team stopped reasoning about the code and simply became assemblers of AI-generated snippets.

Weeks passed.

Merge conflicts became harder. Refactors broke things silently. No one knew which function was the source of truth. Claude had generated hundreds of lines of boilerplate—but the software felt heavier, slower, and more brittle.

Performance issues crept in. Devs spent hours hunting bugs that originated from AI-hallucinated logic. Dependencies bloated. Even small modules took seconds to load. Memory usage doubled. CI pipelines slowed to a crawl.

When a security audit was required, the team realized they didn’t understand half the codebase. “Who wrote this?” became a running joke.

Eventually, the CTO made the hard call:

“We need to pause new development. This is unmaintainable.”

It took a month to clean up—removing redundant modules, consolidating logic, rewriting parts from scratch. The cost was real:

Lost developer trust
Delayed roadmap
Mounting cloud costs
And hundreds of hours sunk into cleanup

1. Security & Reliability Problems in AI Code

Recent research reveals serious vulnerabilities in AI-generated code:

An empirical study found 29.5% of Copilot’s Python and 24.2% of JavaScript snippets contained high-risk security issues across 43 CWE categories.
Follow-up studies show 27% of Copilot-generated code is still insecure, only marginally improved over earlier versions.
A 2021 audit observed that 40% of Copilot’s suggestions contained exploitable flaws, especially when dealing with cryptography, APIs, or auth logic.

2. Context & Memory Are Still Bottlenecks

Despite improving model sizes, LLMs remain fragile at software architecture. They:

Lose context across files
Hallucinate module structure
Struggle with evolving state or long-lived design threads

3. Hallucination & Lack of Explainability

Even if code compiles, agentic AI lacks reasoning or traceability. Developers report:

Code that silently fails logical tests
Missing edge cases
No clarity on why the AI made a specific decision

4. Human Developers Pull Back

Despite popularity, developers reject over 70% of AI-generated code. Key reasons include:

Lack of trust
Need for constant verification
Fear of subtle vulnerabilities or regressions

5. Poisoning & Provenance Risks

Most agentic AI tools train on open codebases—some of which are poisoned intentionally with insecure patterns. Without audit trails or module lineage, this creates:

Propagation of unsafe logic
Loss of trust in AI-written infrastructure
Zero accountability in collaborative enterprise settings

How Agentic AI Tries to Solve These Gaps

Agentic AI papers propose integrating planning and execution capabilities through a structured, iterative architecture that enables autonomous, goal-driven behavior. The core approaches and mechanisms highlighted across recent research include:

1. Sense-Plan-Act Cycle

Agentic AI systems operate on a sense-plan-act cycle: they perceive their environment, formulate a plan based on goals and context, and then execute tasks autonomously. This cycle is foundational, allowing agents to interpret data, plan actions, and adapt execution as conditions change [1][2][3].

2. Task Decomposition and Planning

Upon receiving a goal, the agent breaks down complex objectives into smaller, manageable subtasks. This decomposition is guided by advanced reasoning—using heuristics, search algorithms, or neural networks—to map out the optimal sequence of actions [4][2][5].
Planning frameworks often employ planner-executor architectures, where a planner (often LLM-driven) generates multi-step plans and executor modules carry out each step, leveraging available tools and APIs [2][3].

3. Execution and Feedback Loops

Execution is typically sequential and adaptive: the agent performs each subtask, observes outcomes, and adjusts its plan as needed. This feedback loop is crucial for real-time adaptation and error correction [4][5][3].
Techniques like ReAct (Reasoning and Acting) and ReWOO (Reasoning Without Observation) alternate between reasoning and action, refining the agent’s approach based on new information [5][3].

4. Memory and Context Handling

Agents utilize both short-term and long-term memory systems to track task progress and recall past decisions, enabling coherent multi-step execution and learning from experience [3].

5. Multiagent Collaboration

For complex tasks, agentic AI can use multiagent architectures where agents are assigned specific roles (e.g., planner, executor, reviewer) and collaborate via defined protocols. Leader agents may coordinate teams, improving efficiency and adaptability [1].

6. Tool and API Integration

Action frameworks are built to interface with external tools (APIs, databases, workflow orchestrators), granting agents the ability to perform real-world operations and automate workflows end-to-end [3].

7. Monitoring and Human Oversight

Robust monitoring, logging, and human-in-the-loop mechanisms are integrated to ensure transparency, reliability, and the ability to escalate complex or ambiguous situations to humans [3].

Summary Table: Core Integration Mechanisms

Mechanism	Description
Sense-Plan-Act Cycle	Perceive environment, plan actions, execute, and adapt iteratively
Task Decomposition	Break down goals into actionable subtasks
Planner-Executor Pattern	LLM-driven planning + tool-based execution
Feedback Loops	Continuous observation, adjustment, and learning
Memory Systems	Track state and recall past actions/decisions
Multiagent Collaboration	Specialized agents coordinate and execute in parallel
Tool/API Integration	Agents execute real-world actions via external services
Monitoring & Oversight	Logging, error handling, and human escalation

By combining these elements, agentic AI systems achieve autonomous, adaptable, and transparent planning and execution, moving beyond reactive automation to deliver robust, goal-oriented solutions in dynamic environments [1–10].

Final Thoughts

We’re at the very beginning of the agentic AI journey. Current tools help, but they:

Can’t yet replace the architecture skills of experienced developers
Lack safeguards for production-grade deployment
Don’t offer provable correctness or auditability

That’s not a flaw—it’s a frontier. A call for researchers, builders, and AI engineers to step in with rigor, openness, and curiosity.

Curious? Simtel.AI is building the next generation of AI software infrastructure in stealth. We’re focused on creating agentic AI systems that are not just powerful, but also safe, explainable, and reliable. If you’re interested in exploring how agentic AI can transform your business, let’s connect at info@simtel.ai

The Story of the Forgotten Code Block​

When Agentic Coding Becomes Expensive Technical Debt​

1. Security & Reliability Problems in AI Code​

2. Context & Memory Are Still Bottlenecks​

3. Hallucination & Lack of Explainability​

4. Human Developers Pull Back​

5. Poisoning & Provenance Risks​

How Agentic AI Tries to Solve These Gaps​

1. Sense-Plan-Act Cycle​

2. Task Decomposition and Planning​

3. Execution and Feedback Loops​

4. Memory and Context Handling​

5. Multiagent Collaboration​

6. Tool and API Integration​

7. Monitoring and Human Oversight​

Summary Table: Core Integration Mechanisms​

Final Thoughts​

References​