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Home > Blog > ReAct vs. Pre‑Act: Reinventing Enterprise AI Agents with Fine tuning

ReAct vs. Pre‑Act: Reinventing Enterprise AI Agents with Fine tuning

Neha GuptaNeha GuptaSenior Director of AIUniphore

3 min read

In today’s enterprise landscape, large language models (LLMs) are no longer just chatbots—they are autonomous agents capable of executing multi-step workflows, interacting with knowledge bases, and performing actions across complex systems. Two paradigms for guiding LLM behavior have emerged as leaders: ReAct and Pre‑Act.

At Uniphore, we identified a key limitation in existing methods, leading us to invent Pre‑Act, a framework specifically designed for structured enterprise workflows.

Understanding ReAct

ReAct, introduced in “Synergizing Reasoning and Acting in Language Models” (Yao et al., 2022), emphasizes interleaving reasoning and action:

  • Thought: The model generates internal reasoning
  • Action: The model executes a tool, API call, or external function
  • Observation: The outcome informs the next reasoning step

This creates a dynamic feedback loop that continues until a final answer can be produced.

Example: Vaccination Assistant (ReAct)

Human: Are people with high blood pressure eligible for vaccination?

Thought: Query the Knowledge Base for high blood pressure eligibility.
Action:
{
  "action": "Knowledge_Base_Query",
  "action_input": {"query": "High blood pressure eligibility", "access_token": "ACCESS_TOKEN_123"}
}
Observation: Individuals with high blood pressure are eligible.

Thought: Respond to the user.
Action:
{
  "action": "Final Answer",
  "action_input": "Yes, individuals with high blood pressure are eligible. Anything else I can help with?"
}

Strengths:

  • Flexible and reactive to unpredictable user inputs
  • Well-suited for zero-shot reasoning

Limitations for enterprises:

  • Multi-step workflows can become inconsistent
  • Compliance and auditability are harder to enforce
  • Critical steps may be skipped if conversation changes mid-flow

Introducing Pre‑Act

Pre‑Act (arXiv 250a5.09970, 2025) improves upon ReAct for multi-step, structured enterprise workflows. The model plans its reasoning and actions upfront, then executes the plan step by step.

Key Features of Pre‑Act:

  1. Planning Phase: Generate a multi-step plan before execution
  2. Execution Phase: Follow the plan to ensure consistency
  3. Compliance Embedding: Include regulatory and procedural requirements in the plan
  4. Fine-Tuning Friendly: Models learn structured planning patterns, improving performance on complex tasks

Example: Vaccination Assistant (Pre‑Act)

Human: Can you tell me if people with high blood pressure are eligible and also help me book an appointment?

Thought (Planning): The user has two goals:
1. Check vaccination eligibility.
2. Book an appointment.

Previous Steps:
NA
Next Steps:
Step 1: I will Query Knowledge Base with query: Are individuals with high blood pressure are eligible for vaccination?.
Step 2: Based on the knowledge base response, I will Respond with eligibility info.
Step 3: I will call Check_Agent_Availability tool to check whether a live agent is currently available to handle the appointment request.
Step 4: Transfer call if agent is available.
Step 5: After the transfer, I will conclude the interaction.

Execution Phase:
Action (Step 1): Knowledge_Base_Query(...)
Observation (Step 1): Eligible.
Action (Step 2): Final Answer("Yes, eligible")
Action (Step 3): Check_Agent_Availability(...)
Observation (Step 3): Agent available
Action (Step 4): Call_Transfer(...)
Action (Step 5): Final Answer("Connected to agent. Thank you!")

Advantages for enterprises:

  • Predictable multi-step workflows
  • Built-in compliance and audit trails
  • Reduced errors and hallucinations
  • Optimized fine-tuning and generalization for enterprise tasks

ReAct vs. Pre‑Act: Key Differences

AspectReActPre‑ActEnterprise Advantage
ReasoningInterleaved with actionPlan firstEnsures end-to-end workflow adherence
Tool chainingReactivePre-planned sequenceReduces missed steps across internal systems
ObservationGuides next stepConfirms planAudit and compliance-friendly
Fine-tuningStepwise, dynamicStructured, multi-stepBetter generalization and less hallucination
Error handlingStep-by-stepContingency planning upfrontMore robust in complex workflows

Why Pre‑Act Excels in Enterprise AI

Enterprise AI workflows often involve multi-step operations across CRMs, knowledge bases, ticketing systems, and regulatory procedures. Pre‑Act allows these workflows to be planned, executed, and audited consistently.

Example: Enterprise Vaccination Call Center Workflow

Steps:

  1. Check eligibility via Knowledge Base
  2. Validate insurance and identity
  3. Book appointment in scheduling system
  4. Log interaction in CRM
  5. Deliver compliance messages
  • ReAct: Flexible but may skip steps or miss compliance checks
  • Pre‑Act: Plans all steps upfront, guaranteeing compliance, consistency, and traceability

Other enterprise domains where Pre‑Act shines:

  • Customer support with multiple integrated tools
  • Procurement approval workflows
  • IT incident resolution
  • HR onboarding
  • Financial or insurance risk checks

Results

Results on two in-domain test sets (Glaive and proprietary dataset) and one out-of-domain set (Almita). “Van.” refers to the vanilla (pretrained) model, while “f.t.” denotes fine-tuned. Higher values indicate better performance. Bold numbers highlight the better approach between ReAct and Pre-Act, while bold and underlined numbers indicate the best overall model in each column.

Conclusion

While ReAct is best for reactive, zero-shot reasoning tasks, Pre-Act is best for structured, multi-step enterprise workflows requiring compliance, auditability, and robustness. Based on these advantages, and the results that support them, it is clear: Pre‑Act is the next generation of enterprise AI fine tuning, giving businesses confidence that complex processes will execute reliably—every time.

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