Written for hackers who don’t buy bullshit
TL;DR
The recent wave of “Agentic AI” whitepapers and LinkedIn posts is not a technical breakthrough, but a rebranding stunt. These documents are not blueprints. They are corporate image management PDFs dressed up as innovation.
What’s marketed as “agentic orchestration” is often just LLMs in a loop, with zero state, zero autonomy, and zero guarantees. The so-called “executive playbook” from PwC is a prime example of this trend.
What They Claim
“Agentic AI enables multimodal orchestration, autonomy, goal-driven reasoning, and business transformation across all sectors.”
Buzzword salad? Yes. Let’s break it down.
Supposed Capabilities:
- Autonomy
- Multimodal interaction
- Goal-directed behavior
- Workflow orchestration
- Learning and adaptation
- Inter-agent collaboration
Sounds like AGI, right? But…
What They Actually Show
Not a single architecture.Not a single flow diagram.Not a single open-source agent system with memory, intent, and long-term state.
All they have are:
- Descriptions of existing ML systems (Siemens predictive maintenance, Amazon recommendations, JPMorgan NLP doc analysis)
- Loosely repackaged as “agentic”
- No evaluation metrics
- No benchmark datasets
- No reproducibility
Technical Proof:
Every case study in the document – from Siemens to Netflix – relies on:
- Traditional supervised learning
- Possibly some RAG (retrieval augmented generation)
- No true agentic autonomy or runtime planning
- No real-time goal reasoning or meta-level adaptation
Agent = Wrapper around GPT
If youэve used:
- AutoGPT
- BabyAGI
- LangGraph
- AutoGen
- CrewAI
Then you know: they’re all execution loops with GPT calls, function triggers, and a JSON context.They’re not intelligent. They’re brittle and static.
None of these tools support:
- Episodic memory
- Goal negotiation
- Cross-agent dynamic delegation
- Adaptive planning with unknown inputs
Why This Happened
This is just AI’s Instagram moment – instead of selfies, we now post PDFs with diagrams of arrows pointing at the word “agent”.
Corporate incentives:
- Boards need to show they’re not late to AI.
- Executives need deliverables that look like “strategy”.
- Consultants need to sell transformation services.
Enter: 40-page PDFs with phrases like “from copilot to autopilot” and “service-as-a-software”.
Reality Check
“Agentic AI” in 2024 =
for (const step of task) {
const reply = await gpt(prompt + history);
if (reply.includes('search')) callSearchAPI();
}
That’s it. That’s the agent.
What Needs to Exist (But Doesn’t Yet)
A real agentic system would require:
- Memory: Episodic, semantic, vectorized
- Planning: Abstract goal decomposition and re-planning
- Meta-reasoning: Know when you’re failing
- Action space: Control APIs, tools, services
- Feedback: Environment sensing, consequences
- Autonomy: Operate without script or user babysitting
None of this is present in any “agentic AI” marketed publicly.
Conclusion
Calling current LLM wrappers “agents” is like calling Excel macros a programming language revolution.
Real agents are still an R&D dream. What you see on LinkedIn is marketing cosplay.
Hackers beware: don’t fall for the .pdf industrial complex.
Bonus
If it doesn’t have memory, planning, or an independent action space – it’s not an agent. It’s a prompt with lipstick.
