AgenticLab: Modular Agent-Based AI

• AgenticLab is a modular, agent-based AI framework that decomposes tasks into specialized agents for perception, reasoning, and actuation.
• It operationalizes scientific and robotic workflows using explicit communication pipelines and closed-loop verification for reproducibility and benchmarking.
• Its applications span robotics, medical imaging, and lab optimization, leveraging LLMs, VLMs, and workflow automation to enhance performance.

AgenticLab refers to a class of modular, agent-based AI systems and platforms that operationalize scientific and robotic workflows via autonomous, specialized subagents, explicit multi-stage reasoning, and often closed-loop verification. Recent research has instantiated the AgenticLab paradigm in domains such as real-world robotics, medical imaging, and laboratory operational optimization, leveraging contemporary advances in LLMs, vision-LLMs (VLMs), and LangGraph workflow composition. AgenticLab systems are characterized by their agentic decomposition—encapsulating perception, reasoning, and actuation into distributed components that interact according to explicit communication and data schemas. Their primary aims are to enable reproducible, extensible, and explainable automation in complex, open-ended environments with minimal human supervision (Guo et al., 2 Feb 2026, Li et al., 24 Sep 2025, Fehlis, 23 May 2025).

1. Conceptual Foundations and Scope

AgenticLab architectures are defined by the deployment of multiple, semantically-specialized agents orchestrated over explicit communication graphs or pipelines. These agents communicate via strongly-typed artifacts (e.g., scene graphs, PDDL plans, workflow DAGs, SQL queries) and operate with partial autonomy, often integrating verification-and-repair loops to ensure robustness.

Key architectural features include:

• Modularity: Each agent is responsible for a well-defined subtask (e.g., perception, task decomposition, planning, verification, actuation, reporting).
• Closed-loop operation: Sensing, actuation, and verification (sometimes with replanning) form an interleaved pipeline, contrasted with classical end-to-end or static decision-making.
• Reproducibility and benchmarking: Platforms provide standardized hardware/software stacks and datasets, enabling benchmarking of agentic performance on real-world tasks.

AgenticLab is both a paradigm (for structuring agentic AI workflows) and the name of a specific open-world robotic benchmark and platform (Guo et al., 2 Feb 2026).

2. AgenticLab in Robotics: Architecture and Workflow

The canonical AgenticLab system is a model-agnostic robot agent platform and benchmark designed for deploying VLM-based agents on real-world manipulation tasks (Guo et al., 2 Feb 2026). Its architecture integrates hardware, software, and benchmarking as follows:

Hardware:

• UR5e robot arm on a mobile base.
• Azure Kinect (shoulder-mounted), Intel RealSense D405 (wrist-mounted) for dual-view RGB-D sensing.
• Custom 3D-printed parallel gripper.
• Onboard industrial PC (Intel i9, RTX A4000).

Sensing & Calibration: Both cameras are hand–eye calibrated to obtain transforms $T_{B \to K}$, $T_{B \to W}$, allowing 2D-3D projection: $$p_B = T_{B \to C} \cdot (d \cdot K^{-1}[u, v, 1]^T)$$.

Software Pipeline:

1. See: Multi-view open-vocabulary detection (VLM/LangSAM), scene graph construction, depth-lift bounding boxes; grounding consistency $G_c$ is computed across rollouts.
2. Think: Natural-language goals are parsed into PDDL plans using a VLM (e.g., Gemini, GPT-4o); Fast Downward planner produces action sequence $\pi = \langle a_1, \ldots, a_M \rangle$.
   • Each action's pre- and post-conditions are verified by querying a VLM on real observations.
   • Candidate grasps $g_k$ are scored as $$\alpha P_{\text{semantic}} + \beta P_{\text{collision}} + \gamma P_{\text{stability}}$$.
   • Failure at any verification step triggers replanning.
3. Act: Primitive-based execution (pick, place, open) with Cartesian path planning and closed-loop control.
4. Verify and Replan: Continual verification ensures consistency; failures (e.g., object not detected, condition not met) re-route control to the planner.

Reproducibility: Full stack (mechanical drawings, CAD, code, Dockerized ROS, VLM interfaces) is open-sourced at https://github.com/AgenticLab. The benchmark suite covers sorting, stacking, crossword (spatial logic), reorientation, and kitchen manipulation.

3. Applications in Science and Medical AI

AgenticLab principles extend to non-robotic settings, notably:

• Medical Imaging (“TissueLab”): A modular, co-evolving agentic AI system for medical image analysis, comprising entrance interpretation, LLM-driven workflow planning (DAG over standard plugin nodes), real-time interactive visualization, and an active learning loop for expert corrections. Workflow nodes correspond to data-parallel operations (segmentation, classification), all results and intermediate artifacts are versioned and addressable via an HDF5 memory layer (Li et al., 24 Sep 2025).
  • Task specification and tool selection are formalized as

  $i^* = \arg\max_i U(T_i, Q; \theta)$

  where $U(\cdot)$ is a learned utility or LLM-derived score. - Co-evolution is achieved via in-GUI correction, rapid prototype/fine-tune, and versioning of all light-weight models.

• Lab Workflow Optimization (“Cycle Time Reduction Agents”, CTRA): Agentic decomposition for lab bottleneck diagnosis. Question Creation, Operational Metrics (Query Builder/Validator/Error Analyst), and Summarization Agents interoperate over a LangGraph that maps the flow of queries, code, and results. Bottleneck and cycle time statistics are evaluated mathematically:

  $$t_j = \text{completed\_timestamp}_j - \text{created\_timestamp}_j$$

  Groups with maximized

  $\mu_g = \operatorname{mean}(\{ t_j | j \in g \})$

  and error rates $e_g$ are prioritized as bottlenecks (Fehlis, 23 May 2025).

4. Evaluation Methodologies and Benchmarking

AgenticLab systems employ rigorous, task-specific metrics and physical benchmarks:

• Robotics (Guo et al., 2 Feb 2026):

  • Trial Success Rate (SR): Proportion of trials meeting all goal predicates.
  • Time-to-Completion ($\bar{T}$): Mean completion time over successful runs.
  • Partial Progress Score (P): Fraction of planned actions completed with effect predicates holding.
  • Grasping Error ($\epsilon_g$): Mean Euclidean distance between executed and target grasp points.
  • Bounding-box IoU: Assesses object detection quality across frames.
  • Observed, real-world failure rates: sub-0.7 grounding consistency in 45% of trials; up to 35% precondition check failures under occlusion; only 20% spatial-reasoning task success for complex relational tasks.
• Medical Imaging (“TissueLab” (Li et al., 24 Sep 2025)):
  • Task-level metrics: Dice, IoU (segmentation), AUC (classification), Pearson correlation for continuous outputs.
  • Cohort-level outcome: Achieved higher accuracy and F1 versus GPT-5 and VLM baselines (e.g., lymph-node counting accuracy 0.919 vs. 0.563 for GPT-5).
• Lab Operations (CTRA (Fehlis, 23 May 2025)):
  • Cycle times and error rates per workflow ID and state.
  • Systematic identification of top-k bottleneck workflows via descriptive statistics.

5. Failure Modes, Limitations, and Design Implications

Empirical evaluations across domains surface several characteristic failure scenarios:

• Grounding inconsistencies: VLM-based object references drift across camera views; $G_c < 0.7$ induces repeated replanning cycles.
• Occlusion/scene change sensitivity: Failures in detection under partial visibility; up to 35% precondition verification failure.
• Spatial relations and reasoning: E.g., crosswords and “between” relations reliably misinterpreted (20% success).
• Verification error compounding: With per-step action checker accuracy $p$, overall n-step plan success diminishes rapidly ($p^n$), typically yielding 53% for 6 steps at 90% per-step accuracy, with empirical overall failures explained >60% by verification errors.
• Performance bottlenecks in lab workflows: Single workflow IDs may contribute >95% of all error states, indicating that targeted intervention can dramatically reduce operational delay (Fehlis, 23 May 2025).

These insights inform ongoing and future design directions:

• Need for improved multi-modal grounding and spatial reasoning in VLMs.
• Explicit support for human-in-the-loop correction, especially where verification/repair fails.
• Fine-grained explainability and memory versioning to support co-evolution and robust troubleshooting.

6. Ecosystem, Extensibility, and Future Directions

AgenticLab platforms are released as reproducible, extensible open-source stacks, supporting transparent adoption and modification:

• AgenticLab Robotics: Full hardware/software protocols, calibration, and benchmarking code are accessible for community replication and module development (Guo et al., 2 Feb 2026).
• Medical Imaging (TissueLab): Tool registration is modular (YAML manifest, TaskNode subclass, tool registry hook), supporting rapid prototyping of new algorithms and immediate agentic invocation (Li et al., 24 Sep 2025).
• Scientific Laboratory Automation: LangGraph agent frameworks admit domain adaptation via prompt/function customization, data store modularization, and domain-specific agent tweaks (Fehlis, 23 May 2025).

Planned or suggested extensions of AgenticLab-style platforms include:

• Incorporation of robotic actuation into more scientific domains (materials science, genomics).
• Agentic decompositions for neuro-symbolic programming and other DSL-based system domains.
• Integration of theorem provers, SMT solvers, and fine-grained semantic verifiers for stronger correctness guarantees.
• Mixed-initiative prompting and more robust human-agent cooperative loops.

The AgenticLab paradigm represents a shift toward reproducible, modular, and verifiable AI systems synthesizing perception, reasoning, and action in both physical and computational scientific environments. Its instantiations accelerate benchmarking, diagnosis, and iterative development for real-world AI agents (Guo et al., 2 Feb 2026, Li et al., 24 Sep 2025, Fehlis, 23 May 2025).