How to leverage new AI tools for business optimization in 2026
1. Exa Agent Ultra: In-depth research and large-scale list creation
Exa has launched Agent Ultra, the high-commitment mode of its Agent API. It coordinates thousands of sub-documents to generate comprehensive lists and exhaustive research data. For businesses needing competitive benchmarking or qualified lead lists, Agent Ultra is a powerful tool.
Prompt example
Prompt: "Generate a list of 50 potential B2B partners for our SaaS software house, including company, industry, size, estimated revenue, and data source."The system distributes sub-documents across academic sources, market reports, and third-party databases, returning structured JSON ready for import. The result: significant savings in manual work and access to always up-to-date data.
2. AI coding agents for enterprise software development
By 2026, AI-powered coding tools like GitHub Copilot, AWS Kiro, Cursor, Devin, and Windsurf have reached a level of maturity suitable for large enterprises. When choosing an agent, consider three key factors: allowance coverage, data residency, and cost scalability.
Quick comparison
- GitHub Copilot
- AWS Kiro
- Devin
Example of using Copilot within an enterprise repository:
# Integration with GitHub Actions for automatic documentation generation
# .github/workflows/doc-gen.yml
codegen:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Generate README
run: |
ai docgen --repo . --output README.md --style corporateThis snippet demonstrates how to automate documentation creation, freeing engineers from repetitive tasks.
How to use it
The model accepts a context, a question, and a list of options (from 2 to 20), then returns a ranked list of probabilities.
# Example in Python with the Supersonic SDK
from supersonic import Julia1
model = Julia1(model_path='julia1_v1.pt', device='cpu')
context = "The customer has a low credit score and a stable income."
question = "Which loan offer is most suitable?"
options = ["Secured loan", "Unsecured loan", "Revolving credit line"]
result = model.predict(context, question, options)
print(result)
# Output: [("Secured loan", 0.62), ("Revolving credit line", 0.28), ("Unsecured loan", 0.10)]With this tool, you can quickly perform risk assessments, personalized recommendations, or any other classification task directly on existing corporate servers.
4. Prompt engineering techniques to maximize results
Regardless of the tool you choose, the quality of the prompt determines the quality of the output. Follow these best practices:
- Be specific: Indicate the desired format, length, and level of detail.
- Include constraints: Add style tags, corporate regulations, or privacy guidelines.
- Use context: Provide background information, reference data, or examples.
Example of an optimized prompt for Agent Ultra:
Prompt: "Generate a comparative table of 30 competitors in the European logistics industry. For each competitor, include: name, 2025 revenue, number of employees, regions covered, and main data sources. Output format: JSON."Actionable takeaways
- Start with Exa Agent Ultrafor any data research or list creation needs. A single prompt is enough to get structured results.
- Implement an AI coding agentwithin your CI/CD pipeline to automate code and documentation generation, reducing development time.
- Adopt Julia 1if you have classification workloads that can run on standard CPUs, avoiding additional GPU costs.
- Standardize prompt templateswith the above models to ensure consistency and compliance across your organization.