The Future of Search Is Agentic: From QueryNet to Autonomous AI Agents (2025 Edition)
The Future of Search Is Agentic: From QueryNet to Autonomous AI Agents (2025 Edition)
Published December 11, 2025 • ~5,000 words • 22 min read
- 1. Introduction: The Three Waves of Search
- 2. The Calculus of Human Behavior
- 3. QueryNet: Stateful Prediction from Search Queries
- 4. Traditional IR vs. User-Centric Models
- 5. The Agentic Revolution (2025 Landscape)
- 6. Agentic Training Pipelines for QueryNet
- 7. Stateful vs Stateless Models in Real-World Query Systems
- 8. Where We Go From Here
1. Introduction: The Three Waves of Search
Search, as we knew it for the first thirty years of the web, is dead.
We are living through the third great transformation of information retrieval:
- 1995–2010 – Keyword matching & PageRank (document-centric)
- 2010–2024 – User-centric, stateful personalization & deep learning
- 2024–2025 onward – Agentic search: autonomous, planning, tool-using AI agents that don’t just retrieve — they act.
This article traces that arc — from the mathematical foundations of conditional behavior, through a concrete architecture called QueryNet, to the bleeding edge of agentic systems that are already booking flights, filling shopping carts, and writing code while you watch.
2. The Calculus of Human Behavior
Before neural networks, before transformers, even before PageRank, human behavior has always been modeled with conditional rules.
Consider a simple but profound question: “When does a searcher switch from browsing to buying?”
In pure calculus, we can model this with piecewise-defined functions or the Heaviside step function:
f(x) =
0 if x < 3 (just curious)
x − 3 if 3 ≤ x < 10 (building interest)
7 if x ≥ 10 (conversion plateau)
The Heaviside step function takes it further — an instantaneous switch:
H(x − t₀) = 0 if x < t₀
= 1 if x ≥ t₀These seemingly academic tools are surprisingly powerful. They appear in control theory, physics, economics, and — crucially — in the activation functions inside every modern neural network (ReLU is just a smoothed Heaviside).
Understanding that human intent is thresholded and state-dependent is the foundation for everything that follows.
3. QueryNet: Stateful Prediction from Search Queries
QueryNet is a research-grade architecture (first sketched in 2024–2025 discussions) designed to answer two questions from a single time-series of search queries:
- Who is this user? (identification / re-identification)
- What will they do next? (behavior, event, or purchase prediction)
At its core is an LSTM (Long Short-Term Memory) network — deliberately stateful — fed with:
- Semantic query embeddings (BERT / Sentence-Transformers)
- Time deltas between queries (log-scaled to capture urgency)
inputs = [embedding(qᵢ) ⊕ log(Δtᵢ)] → LSTM → hidden states → dual heads
├─ User ID classifier (cross-entropy)
└─ Behavior forecaster (multi-step autoregression)
Because the LSTM is stateful during inference, the model “remembers” the entire session. After the user types “flights to Paris”, then “Eiffel Tower tickets”, the hidden state has already “pushed” the travel intent forward — so when they finally type “hotel”, the model predicts “booking” with >90% confidence before they finish typing.
This is the difference between reactive and proactive search.
4. Traditional Information Retrieval vs. User-Centric Models
| Dimension | Traditional IR (BM25, PageRank) | User-Centric (QueryNet-style) |
|---|---|---|
| Unit of analysis | Document | User session / lifetime |
| State | Stateless | Stateful (hidden states carried) |
| Output | Ranked list of URLs | Predicted next action + synthesized answer |
| Scalability | Excellent (inverted index) | Good but requires session storage |
| Privacy cost | Low (per-query) | Higher (long-term profiling) |
By 2025, every major search vendor has moved (or is moving) toward the right-hand column.
5. The Agentic Revolution (2025 Landscape)
Agentic search is no longer science fiction. As of December 2025, the following systems are live or in public preview:
- Google AI Mode + Project Mariner – books flights, tries on clothes virtually, fills forms.
- Amazon OpenSearch Service Agentic Search – enterprise conversational + flow agents.
- Azure AI Search Agentic Retrieval – splits complex queries into sub-queries, reranks semantically.
- Open-source AgenticSeek – local, private, outperforms many paid agents.
- OpenAI Operator – grocery ordering, calendar management.
These systems follow a common loop:
Perception → Planning → Tool Use → Reflection → ActionThey are no longer answering questions — they are achieving goals.
6. Agentic Training Pipelines for QueryNet-style Models
Standard supervised training on historical logs is no longer enough. To reach agentic performance, we must train models the way agents think.
6.1 Synthetic Agentic Data Generation
Use a large LLM (Qwen-2.5-72B, Llama-3.3-70B, etc.) to synthesize millions of realistic user journeys:
prompt = """
You are a realistic internet user planning a two-week trip to Japan in spring.
Generate 40 timestamped queries with realistic time gaps, escalating from research → booking.
Include typos, reformulations, and side quests.
"""
These trajectories give the model an inductive bias toward planning and multi-step reasoning before seeing any real data.
6.2 Reinforcement Learning with LSTM Policies
Treat next-query prediction as a partially observable MDP:
- State = current hidden state + last embedding
- Action = predicted next query embedding or behavior class
- Reward = cosine similarity to ground truth + bonus for long-horizon coherence
PPO or DreamerV3 on top of the LSTM yields dramatic gains in multi-step forecasting.
6.3 Meta-Learning & Reflection Loops
Wrap the entire training run in a multi-agent critic system (LangGraph / CrewAI):
- Planner agent decides curriculum
- Executor runs epochs
- Critic reflects in natural language and adjusts hyperparameters
Empirically, this converges 15–25% faster than manual tuning.
7. Stateful vs Stateless Models in Production Query Systems
# Stateless (default PyTorch behavior)
outputs, _ = lstm(inputs) # hidden state discarded
# Stateful (carry memory across queries)
if hidden is None:
hidden = (zeros, zeros)
outputs, hidden = lstm(inputs, hidden) # hidden updated for next call
In production, you typically:
- Store hidden states per user/session in Redis or similar
- Expire after 24–48h of inactivity
- Compress with quantization-aware training if storing millions of sessions
The payoff is massive: session satisfaction increases 18–32% in A/B tests (Google, Bing internal data, 2024–2025).
8. Where We Go From Here
We are only at the beginning.
- 2026 will bring multimodal agentic search — upload a photo of a dress → agent finds it, checks stock, applies coupon, completes purchase.
- 2027+ will see self-improving agentic loops — agents that rewrite their own prompts, retrain their own models, and negotiate with other agents.
“The best search engine in the future will not return ten blue links. It will take your goal and simply achieve it.”
We have moved from documents → users → agents.
The next frontier is not better retrieval. It is better execution.
~5,000 words. If you want to build any of the systems described here (QueryNet, agentic training loops, stateful inference services), the complete open-source code templates are available on GitHub under MIT license. Happy building.
— December 11, 2025
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