Foundation Model-Based Agents

Updated 6 September 2025

Foundation model-based agents are autonomous systems that leverage large pretrained models for integrated reasoning, planning, and action execution.
They combine sequence-based modeling with agentic control to achieve complex decision-making and multi-agent coordination across diverse modalities.
These agents are applied in robotics, web automation, healthcare, and scientific discovery, emphasizing safety, observability, and scalability in deployment.

Foundation model based agents are autonomous systems whose core reasoning, planning, and action capabilities are powered by large pretrained models—primarily LLMs, vision–LLMs (VLMs), or more general multimodal foundation models—originally developed for broad tasks in language and perception. These agents leverage the expressive representations, reasoning patterns, and cross-domain knowledge in foundation models to perform complex sequential decision-making, plan generation, multi-agent coordination, and real-world action. Their emergence represents a convergence of generative, sequence-based modeling with agentic systems, yielding new paradigms for architecture, training, observability, safety, and deployment.

1. Core Principles and Conceptual Foundations

Foundation model based agents unify techniques from classical agents and modern foundation models (Yang et al., 2023). Their central characteristics include:

Unified Representation: Agents encode inputs, states, actions, and planning traces using shared tokenization or embedding schemes (text, vision, action)—enabling unified policies across domains (Liu et al., 27 May 2024). For example, in the Interactive Agent Foundation Model, visual, textual, and action tokens are fused into a joint transformer backbone (Durante et al., 8 Feb 2024).
Decision Making via Sequence/Generative Modeling: Decision making is reframed as sequence modeling: agents predict action (or plan) tokens conditioned on their multimodal context, often drawing on paradigms like Decision Transformers or LLMing losses (Yang et al., 2023, Liu et al., 27 May 2024).
Prompt/Plan-Centric Control: Control and replanning are mediated by prompting (natural language descriptions, goals, tool APIs) and/or structured plan generation (single-path, multi-path, chain-of-thought, or tree-of-thought) (Lu et al., 2023, Liu et al., 16 May 2024).
Tool Use and Coordination: The agent reasoning core is augmented with tool-use policies—external APIs, calculators, code execution—invoked based on the plan, with results fed back for further reasoning (Zhou et al., 6 Aug 2024, Li et al., 6 Aug 2025).
Self-Supervised and Multi-Task Learning: Training regimes integrate self-supervised learning (predicting actions, masked tokens), imitation/fine-tuning, reward-based RL, and curriculum learning across multiple data sources and domains (Durante et al., 8 Feb 2024, Liu et al., 27 May 2024).

2. Architectures, Patterns, and Taxonomies

Recent work provides systematic taxonomies and reference architectures outlining key modules, design patterns, and selection criteria (Lu et al., 2023, Zhou et al., 6 Aug 2024, Liu et al., 16 May 2024, Xu et al., 18 Dec 2024):

Input and Goal Creation: Passive and proactive goal creators handle user instructions via dialogue, context, and multimodal cues.
Prompt/Response Engineering: Templates and optimizers structure prompts for foundation models, integrating retrieval augmentation when model priors are insufficient (Liu et al., 16 May 2024, Tian et al., 20 Oct 2024).
Planning and Plan Generators: Single-path (linear reasoning) and multi-path (exploratory, branching, ToT/CoT) plan generators, refined via self-, cross-, or human-reflection loops.
Memory Systems: Short-term (within-context) and long-term (retrieved, possibly semantic/vectorized) memory modules handle working state and augment foundation model context windows (Lu et al., 2023, Zhou et al., 6 Aug 2024).
Execution Engines: Task executors/monitors coordinate plan execution, trigger tool/agent selection, and manage external API calls (Zhou et al., 6 Aug 2024, Fang et al., 1 Aug 2025).
Safety/Responsible AI Plugins: Guardrails, risk assessors, blackbox recorders, explainers, and traceability mechanisms enforce safety, legality, and auditability, often via multi-layered runtime interventions (Lu et al., 2023, Shamsujjoha et al., 5 Aug 2024, Dong et al., 8 Nov 2024).

Architectures may instantiate these modules as explicit agent “roles” (coordinator/worker), agentic submodules (planner, memory, executor), or as implicit functions within a single end-to-end model (see Chain-of-Agents paradigm (Li et al., 6 Aug 2025)).

3. Training Regimes, Self-Play, and Skill Discovery

Training paradigms for foundation model based agents are characterized by:

Multi-Task and Multimodal Pretraining: Agents are exposed to datasets spanning diverse domains—robotics, web navigation, video games, healthcare—using unified encoders and transformer backbones (Durante et al., 8 Feb 2024).
Reinforcement Learning and Behavioral Cloning: Action policies are refined via RL (from scalar, sparse, or evaluator-derived rewards) and behavioral cloning on successful trajectories. For example, the Proposer-Agent-Evaluator (PAE) framework bootstraps new skills via self-generated tasks, chain-of-thought exploration, and VLM-based binary success evaluation, iterating with RL (Zhou et al., 17 Dec 2024).
Agentic Knowledge Distillation: Multi-agent systems and workflows are distilled into “chain of agent” trajectories. Chain-of-Agents (CoA) stores transitions as tuples (state, role, output), which are used for supervised fine-tuning and agentic RL (Li et al., 6 Aug 2025).
Reflection and Voting: Test-time self-reflection, retrying, and trajectory voting are incorporated to increase robustness and reliability (Cognitive Kernel-Pro) (Fang et al., 1 Aug 2025).
Prompt and Task Proposal: Autonomous skill discovery is enabled via context-aware task proposal models, generating new tasks via LLMs that generalize beyond hand-annotated templates (Zhou et al., 17 Dec 2024).

4. Practical Applications and Impact Domains

Foundation model based agents have been deployed or evaluated in a range of high-impact domains (Yang et al., 2023, Liu et al., 27 May 2024, Zhao et al., 10 Dec 2024, Ning et al., 30 Mar 2025):

Domain	Application	Key Attributes/Findings
Robotics	Tabletop manipulation, navigation, drone	Unified vision/language/action models
Web Automation	Form-filling, web search, web scraping	Perception, planning, multi-turn dialogue
Healthcare	Video QA, patient state, diagnosis/care	Context-aware, safety/traceability
Scientific Discovery	Literature mining, synthesis planning	Tool use, memory, cross-domain reasoning
Financial Analysis	Customizable search, document analysis	Retrieval-augmented, vector DBs
Social Impact	Resource allocation (e.g., ARMMAN case)	Human-in-the-loop, fairness, agent sim

Agents are increasingly evaluated using full-stack benchmarks and real-world scenarios, including the GAIA benchmark for research agents (Fang et al., 1 Aug 2025), WebVoyager/WebArena for internet agents (Zhou et al., 17 Dec 2024), and RiskAwareBench for embodied safety (Zhu et al., 8 Aug 2024). Notably, evaluation highlights significant gaps in risk awareness and robustness—task risk rates exceeding 90% are reported for current models in risky physical task planning (Zhu et al., 8 Aug 2024).

5. Trustworthiness, Safety, and Observability

Recognizing the autonomy and unpredictability of FM-based agents, a substantial corpus addresses architecture-level and operational safety (Shamsujjoha et al., 5 Aug 2024, Dong et al., 8 Nov 2024):

Guardrails: Multi-layered, modular defenses filter, block, or flag unsafe behaviors at each pipeline stage—input, intermediate state, plan, execution, and output. Guardrail quality dimensions include accuracy, adaptability, traceability, and interpretability.
AgentOps and Cognitive Observability: DevOps-style observability frameworks (AgentOps, Watson) provide fine-grained traceability of execution, reasoning, planning, tool use, and evaluation (Dong et al., 8 Nov 2024, Rombaut et al., 5 Nov 2024). Surrogate agent shadowing and fill-in-the-middle techniques can reconstruct hidden reasoning, supporting debugging and targeted intervention.
Responsible AI Plugins: Risk assessors, explainers, and black box recorders ensure alignment with security, privacy, and ethical requirements, enabling human oversight and forensics (Lu et al., 2023).
Evaluation of Safety and Robustness: Benchmarks like AdvWeb, ARE, and ST-WebAgentBench test for adversarial robustness and privacy leakage; PrivacyLens measures the exposure of sensitive data during web interactions (Ning et al., 30 Mar 2025).

Despite progress, many agents remain susceptible to adversarial prompts, environmental injection attacks, and plan generation that violates safety tips, even with explicit prompt-based mitigation (Zhu et al., 8 Aug 2024).

6. Scalability, Deployment, and Infrastructure

Deployment at scale motivates extensive work on optimization and resource orchestration (Xu et al., 18 Dec 2024):

Inference Efficiency: Techniques include kernel fusion, dynamic batching, token pruning/compression, low-bit quantization, and knowledge distillation to accelerate model execution and reduce cost.
Parallelization and Load Balancing: Data/model/pipeline parallelism—via auto-parallelization and hybrid resource orchestration—serve distributed workloads and heterogeneous device fleets (edge-cloud, accelerators).
Layered Frameworks: Modular “stacked” architectures decouple execution, model, and agent layers, supporting extensibility for tool integration, memory management, and multi-agent cooperation.
Adaptive Resource Management: Dynamic scaling aligns compute and communication to workload spikes; monitoring resource utilization is integrated with overall agent observability.

Applications cited include chatbots (ChatGPT, AutoGPT), deeply interactive agents for research (Cognitive Kernel-Pro), document analysis, and collaborative systems (e.g., AgentVerse) (Xu et al., 18 Dec 2024, Fang et al., 1 Aug 2025).

7. Trends, Research Directions, and Open Problems

Current and prospective work is oriented toward several research vectors (Yang et al., 2023, Liu et al., 27 May 2024, Li et al., 6 Aug 2025, Xu et al., 18 Dec 2024):

Unified and Compositional Agents: Investigation is ongoing into strongly unified architectures versus modular, specialized composition (integrating SSMs, LLMs, VLMs, and symbolic/planning engines) (Liu et al., 27 May 2024).
Enhanced Generalization and Autonomy: Agents must better generalize to novel domains, unseen tools, and dynamic environments, maintaining safety and traceability.
Autonomous Skill Discovery: Transitioning from static curriculum to self-proposed, context-driven skill acquisition (as exemplified by PAE (Zhou et al., 17 Dec 2024)) is a central research challenge.
Agentic Reinforcement Learning: End-to-end agentic RL—rewarding agent chains for correct, verifiable outcomes—yields significant gains in multi-turn, multi-role problem solving (CoA/AFM paradigm (Li et al., 6 Aug 2025)).
Human-in-the-Loop and Social Alignment: Trustworthy deployment will require more sophisticated human oversight, preference elicitation, explainability tools, and mechanisms for collaborative accountability (Lu et al., 2023, Zhao et al., 10 Dec 2024).
Safety, Responsible AI, and Lifecycle Observability: Developing standardized, interoperable guardrails, safety benchmarks, and observability pipelines is critical for robust and transparent agent operations (Shamsujjoha et al., 5 Aug 2024, Dong et al., 8 Nov 2024).
Scalable, Reproducible Infrastructure: Open-source development frameworks that democratize foundation model agent research while supporting resource-efficient deployment are increasingly emphasized (Fang et al., 1 Aug 2025, Li et al., 6 Aug 2025).

A recurring challenge is bridging the gap between general pretraining and domain-specific performance—a space where mechanisms such as retrieval-augmented generation, external knowledge integration, and modular reflection/voting are seeing rapid advances.

Foundation model based agents represent a confluence of deep pretrained representation and agentic control, enabling new levels of generality, adaptability, and intelligence across domains. Their architecture and training patterns, safety mechanisms, and lifecycle observability form a rapidly expanding research landscape, with strong connections to advances in reinforcement learning, symbolic reasoning, multi-agent systems, and human–AI collaboration. Continued progress hinges on addressing sample-efficient adaptation, robust safety, transparent decision-making, and scalable, responsible deployment.