Platform

Agentic Labs

From Scientific Question to Executable Work

Agentic AI capable of reasoning, integrated with digital twins and self-driving chemical laboratories. Agents interpret goals, gather evidence, propose campaigns, orchestrate tools and labs, evaluate results, and learn, with human oversight at every step.

A robotic arm dispensing into sample vials on an automated lab bench, with a plan, experiment, analyze, and iterate loop floating above.
Agents plan, run, analyze, and iterate, closing the loop from question to result on real lab hardware.

The Agentic Loop

Ten steps from scientific question to actionable results. Each cycle feeds the next, compounding knowledge and improving predictions.

1

Define goal or target profile

Specify what you need: a target material profile, formulation constraints, process requirements, or a multi-objective design brief.

2

Retrieve evidence and prior art

Agents search literature, patents, internal records, and experimental history to build a comprehensive evidence base.

3

Generate candidates or strategies

Propose molecules, materials, formulations, routes, or process conditions using foundation models and domain constraints.

4

Simulate, score, and rank

Evaluate candidates against multiple objectives. Surface tradeoffs, uncertainties, and Pareto-optimal options.

5

Plan experiments

Design experimental campaigns that maximize information gain while respecting budget, time, and resource constraints.

6

Execute via self-driving labs, CROs, robotic platforms, or external lab networks

Orchestrate work across self-adaptive chemical laboratories, contract research organizations, robotic platforms, or partner networks, with digital twin feedback for real-time process adjustment.

7

Analyze outcomes

Process experimental results, compare against predictions, and identify discrepancies that signal new learnings.

8

Update models and confidence

Feed results back into foundation models and the knowledge graph. Every experiment improves future predictions.

9

Recommend next steps

Propose the next round of experiments, simulations, or design iterations based on updated understanding.

10

Track program-level progress

Track convergence toward target profiles, along with resource use and decision readiness, across entire programs.

Specialized Skills

Our agents draw on a library of skills covering every stage of chemical and industrial R&D. Each skill can be composed into larger workflows and campaigns.

Literature & Patent

Searches, extracts, and synthesizes findings from scientific literature and patent databases.

Reaction Planning

Plans synthetic routes, selects conditions, and evaluates retrosynthetic strategies.

Formulation Design

Designs and optimizes formulations across ingredient, performance, and manufacturing constraints.

Engineering Drawing

Reads P&IDs and PFDs, reconciles them into a typed plant graph, and answers bounded questions with evidence pinned to the sheet.

Materials Discovery

Explores materials design spaces and proposes candidates for target property profiles.

Catalyst Design

Identifies, ranks, and optimizes catalyst candidates for target reaction performance.

Process Optimization

Optimizes reaction conditions, process parameters, and manufacturing workflows. Integrates with digital twins for multi-scale dynamic system reasoning.

Safety & Hazard

Assesses chemical hazards, evaluates safety constraints, and flags risks before execution.

Regulatory

Checks compliance requirements, regulatory constraints, and documentation needs.

Experimental Design

Plans statistically sound experiments that maximize information gain per resource spent.

Scale-up Risk

Predicts scale-up challenges, assesses process robustness, and recommends risk mitigations.

Human-in-the-Loop
“Autonomy is graduated, not magical.”

Every agentic workflow is designed with human oversight at its core. Scientists stay in control while agents handle the heavy lifting.

Approval Checkpoints

Review and approve before agents execute critical steps.

Editable Plans

Modify agent-proposed plans at any point in the workflow.

Side-by-Side Comparisons

Compare options with full rationale and uncertainty.

Uncertainty Display

See confidence levels and data quality for every recommendation.

Full Auditability

Complete history of agent actions, decisions, and reasoning.

What “Agentic” Means at Alchemic

Not vague automation. Concrete scientific capabilities that turn reasoning into action.

Decompose a scientific goal into actionable steps
Retrieve and synthesize relevant evidence
Propose experiments or simulations
Operate tools, instruments, and self-driving laboratories
Interact with digital twins for predictive process reasoning
Compare outcomes against predictions across scales
Explain tradeoffs with full rationale
Update beliefs based on new evidence
Recommend next actions with confidence levels
Navigate multi-scale, high-dimensional chemical systems

Put your R&D on a closed loop

See how agentic labs can turn your scientific questions into executable work with evidence-backed decisions at every step.