RTBAgent: Adaptive Real-Time Agents

• RTBAgent is a framework of adaptive agent architectures designed for real-time decision-making in contexts such as online bidding, autonomous control, and security enforcement.
• The systems integrate LLM reasoning, multi-memory retrieval, and optimization techniques (e.g., QP and RL) to meet strict performance and safety constraints.
• Empirical results show that RTBAgent implementations improve efficiency, safety, and resilience to attacks compared to traditional methods in diverse application domains.

RTBAgent is a designation applied to advanced agent architectures for critical real-time decision-making in online advertising, safety-critical multi-agent control, and LLM-based tool-using systems. The term encompasses agents for real-time bidding (RTB) in programmatic advertising, autonomous supervisory safety controllers for spacecraft, and robust LLM-based TBAS security layers. While the implementations and objectives differ, all RTBAgent designs combine real-time adaptive reasoning, modular architecture, and online integration of domain knowledge, optimization routines, or external tool capabilities, under strict performance or safety constraints.

1. RTBAgent in LLM-Based Real-Time Bidding Systems

The most recent instantiation of RTBAgent is an LLM-driven agent system for RTB environments, as described in "RTBAgent: A LLM-based Agent System for Real-Time Bidding" (Cai et al., 2 Feb 2025). The system is engineered for dynamic, high-frequency auctions, where it performs on-the-fly bid price optimization under budget and strategic constraints. The architecture is composed of a central LLM "reasoning core" coupled with a modular toolkit $\mathcal{H}$ (including a click-through rate estimator and expert strategy knowledge base), a multi-memory retrieval mechanism, and a daily reflection loop.

Key pipeline elements:

• Observation and Tool Invocation: At each decision step $t$, the agent observes state $s_t$ (budget, win rate, market stats), invokes the CTR model $\hat{p}_t = f_\theta(x_t)$, and retrieves a base bid factor $\lambda_{base}$ from the expert knowledge base.
• LLM Two-Step Decision Sequence: The LLM summarizes relevant context from multiple memory sources ($\mathcal{M}^{env}$, $\mathcal{M}^{bid}$, $\mathcal{M}^{ref}$), outputs an adjustment $a_t\in[-0.5,0.5]$, and finalizes the bid as $b_{\text{final},i}=b_{\text{base},i} \cdot (1+a_t)$.
• Multi-Memory Retrieval: Query-aware retrieval $R(\mathcal{M},q)$ provides the LLM with history blocks most relevant to current market conditions, supporting real-time adaptation to fluctuations.
• Daily Reflection: Post-episode, the system synthesizes adjustment patterns, CPC/win-rate trade-offs, and proposes prompt or parameter updates for the next day, facilitating continual learning.

Empirical evaluation on the iPinYou RTB dataset (10 days, 19.5M impressions) shows that RTBAgent achieves a click count up to 0.4% higher than the best RL and generative baselines across a wide budget spectrum. Notably, 97% of its chain-of-thought rationales are judged reasonable by human experts (Cai et al., 2 Feb 2025).

2. Agent Design: Functional Optimization for RTB

A distinct RTBAgent architecture applies multi-agent reinforcement learning with functional optimization to RTB, as formulated in "Functional Optimization Reinforcement Learning for Real-Time Bidding" (Lu et al., 2022). The system optimizes over campaign-level bidding functions with Lagrange-multiplier constraints for global budget pacing:

• Mathematical Structure: Given feature vector $x$, each impression is scored for CTR $c(x)$, and the agent's objective is to maximize $J[b]=N\int_c c\,w(b(c))\,P_C(c)\,dc$ subject to a budget $C[b]\leq B$.
• Agent Variants: Four agent types are instantiated—Baseline DQN, FOA II (Lagrange dual in state), FOA III (dual as action), and FOA IV (dual in reward). Each variant leverages the Lagrange multiplier $\lambda$ differently for budget constraint awareness.
• Policy Learning: All agents use DQN with experience replay and $\epsilon$-greedy exploration. Win probability surfaces $w(b)$ are cubic polynomials, learned online under both biased and unbiased sampling regimes.

In simulated auction campaigns, embedding the dual variable $\lambda$ into the agent’s state or reward yields substantial gains in win-rate and cost-efficiency, especially under tight budgets (e.g., FOA III's win-rate reaches 62.1% at $B=250$K budget, compared to $\sim$35% baseline at high budgets). The explicit functional approach enables exploitation of market pacing and budget exhaustion phenomena (Lu et al., 2022).

3. Supervisory RTBAgents in Autonomous Control

Another application, described in "Run Time Assurance for Autonomous Spacecraft Inspection" (Dunlap et al., 2023), defines RTBAgent as a modular, real-time supervisory barrier agent for safety assurance in multi-agent spacecraft missions. Acting as a filter between the nominal controller and the plant, the RTBAgent enforces dynamic Control Barrier Function (CBF) constraints via quadratic programming:

• Control Loop Placement: The system observes state $x$, receives a nominal control $u_{des}$, and solves at each step: $$u_{act}(x,u_{des}) = \arg\min_{u\in U} \|u_{des}-u\|^2$$ subject to multi-dimensional CBF safety constraints.
• Constraint Architecture: Up to twelve dynamically-evolving constraints (collision avoidance, speed, keep-in/out zones, fuel limits, etc.) are formalized as forward-invariant sets via functions $h_i(x)\geq 0$ and strengthened with class-$\kappa$ functions.
• Centralized vs. Decentralized RTBAgent: Centralized agents coordinate multi-agent safety with fewer constraint conflicts (achieving 100% success in 2000 Monte Carlo trials), while decentralized agents scale parallelization at the cost of conservatism and possible constraint infeasibility (failure rate 9.05% traced to overlapped exclusion zones) (Dunlap et al., 2023).

The framework generalizes RTA to arbitrary nonlinear, high-dimensional plant models, offering formal guarantees via Nagumo-type invariance criteria and real-time computational feasibility for moderate agent scales.

4. Security-Focused RTBAgents for LLM Tool-Based Agent Systems

Within Tool-Based Agent Systems (TBAS) employing LLMs with external tool calls, RTBAgent also denotes an architectural theme for runtime policy enforcement, most notably realized as Robust TBAS (RTBAS) (Zhong et al., 13 Feb 2025):

• Information Flow Control (IFC): Every message and tool response is tagged with security lattice labels for integrity ($l_i$) and confidentiality ($l_c$). Each tool's invocation is authorized only if the joined context label flows to the tool's assigned policy.
• Dependency Screening: RTBAgent/RTBAS uses two novel mechanisms to avoid unnecessary user prompts—LM-as-a-judge, which asks an auxiliary LLM to identify which message regions influence the next tool call, and attention-based saliency screening, which uses Taylor expansion metrics and an LSTM classifier to identify critical context.
• Automatic Tool-Call Mediation: Tool actions are executed immediately if their information flow context is policy-compliant; otherwise, a one-time user confirmation is required.
• Attack Prevention: On the AgentDojo benchmark, both screening modes block 100% of prompt injection attacks, with only 1–3% utility degradation relative to no attack, and greatly reduced user prompt fatigue compared to baseline commercial implementations (e.g., OpenAI GPTs) (Zhong et al., 13 Feb 2025).

5. Comparative Properties and Implementation Patterns

The following table organizes the RTBAgent instances according to core properties:

Domain	Optimization/Guarantee Mechanism	Core Modules/Features
RTB LLM Agent (Cai et al., 2 Feb 2025)	LLM-based reasoning, hybrid with CTR & memory	LLM core, multi-memory, 2-step bidding, expert knowledge
Functional Opt. RL (Lu et al., 2022)	DQN with Lagrange dual functional	DQN agents, state/reward/action λ, cubic win-prob models
Spacecraft Supervision (Dunlap et al., 2023)	CBF/QP for multi-constraint safety	Plant observer, ASIF-QP layer, centralized/decentralized modes
TBAS Security (Zhong et al., 13 Feb 2025)	IFC with dependency screening	LM-Judge/Attention screeners, per-tool policies

RTBAgent implementations consistently leverage modular abstraction—decoupling reasoning/optimization/safety policy from low-level execution or environment—and online adaptation via real-time data, memory retrieval, or policy updates. In security-focused settings, label-driven control of information propagation and contextual sensitivity screening are essential to avoid both over-conservatism and integrity violations.

6. Impact, Limitations, and Directions

RTBAgent frameworks in RTB drive measurable uplifts over both rule-based and deep RL solutions in click-volume, cost-efficiency, and adaptive interpretability (Cai et al., 2 Feb 2025, Lu et al., 2022). In safety-critical multi-agent control, formal guarantees (e.g., forward invariance, Monte Carlo safety) are achievable in centralized/hierarchical settings, while decentralized variants require careful tuning and relaxation of couplings (Dunlap et al., 2023). Privacy/security RTBAgents set a new standard for selective mediation in LLM tool-based environments, achieving perfect attack prevention on standard benchmarks at limited utility cost (Zhong et al., 13 Feb 2025).

Limitations center on computational scaling in control QPs, the reliance of LLM-based agents on prompt and model stability, the challenge of policy inference in dynamic multi-agent safety, and increased latency/cost in dependency-screened security enforcement. Extensions under research include hierarchical RTBAgents for combined central/decentral operation, adaptive policy updates, schema-automated region tagging, and coordination across multi-agent TBAS ecosystems.

7. Source Code and Practical Use

The RTBAgent LLM-based RTB implementation is publicly available at https://github.com/CaiLeng/RTBAgent, including modular scripts for execution loop, tool API code for CTR estimation and expert strategies, memory management, and LLM prompt templates (Cai et al., 2 Feb 2025). This enables direct benchmarking, modification, and extension for industrial RTB applications, as well as adaptation to alternative real-time decision domains where interpretability, modularity, and online adaptation are critical.