Multi-modal Agents

Multi-modal capability has expanded significantly:

Native fusion: Models like Gemini 3 and Llama 4 are trained with text and visual content together, not bolted on separately. This produces deeper understanding.

Computer use: Claude Opus 4.5 and Sonnet 4.5 can interact with graphical interfaces, navigating websites and applications to complete tasks. Claude achieves 66.3% on OSWorld benchmarks for real-world computer tasks.

Video understanding: Models can process video content, understanding temporal sequences and extracting information across frames.

Document intelligence: Reading PDFs, forms, tables, and complex document layouts with high accuracy.

Audio processing: Native speech understanding and generation integrated with other modalities.

Multi-modal capabilities enable applications like:

Browser-based automation: Agents that navigate web applications, fill forms, extract data, and complete tasks that previously required humans at keyboards.

Document processing: Extracting information from invoices, contracts, and forms including tables, signatures, stamps, and handwritten annotations.

Visual inspection: Analysing images for quality control, compliance verification, or damage assessment.

Screen understanding: Interpreting application interfaces to automate workflows across enterprise systems.

Video analysis: Processing meeting recordings, surveillance footage, or instructional content to extract insights or summaries.

Customer support: Understanding photos of products, screenshots of error messages, or visual descriptions of problems.

Current multi-modal models offer varying strengths:

Gemini 3 provides exceptional multimodal understanding with native support for text, images, video, and audio in a unified architecture.

Claude Opus 4.5 leads on computer use capability, with the ability to interact with graphical interfaces reliably.

GPT-5.2 offers strong vision understanding integrated with extended reasoning capabilities.

Llama 4 brings multimodal capability to open-weight models, with Scout and Maverick understanding both text and images.

We select appropriate models based on your specific requirements and task characteristics.

The ability to interact with computer interfaces opens significant possibilities:

Web automation: Completing tasks that span multiple websites and applications.

Legacy system interaction: Automating processes in systems without APIs by interacting with their user interfaces.

Testing and QA: Automated testing that interacts with applications as users would.

Data migration: Extracting and transferring data between systems through their interfaces.

Monitoring and reporting: Gathering information from dashboards and systems that require visual access.

This capability is still maturing but already useful for appropriate use cases.

Creating effective multi-modal agents requires:

Input handling: Processing different content types and preparing them appropriately.

Prompt design: Instructing models effectively for visual and multimodal tasks.

Output parsing: Extracting structured information from model responses.

Action execution: For computer use, reliably executing intended actions.

Error handling: Managing cases where visual content is unclear or actions fail.

Quality assurance: Verifying visual understanding and action accuracy meet requirements.

Multi-modal capability adds complexity:

Data volume: Images, video, and screen captures are larger than text, affecting latency and costs.

Action safety: Computer use agents need guardrails to prevent unintended actions.

Quality sensitivity: Results depend on input quality. Poor images produce poor analysis.

Confidence calibration: Understanding when interpretation is reliable versus uncertain.

We design systems that handle these considerations appropriately.

Multi-modal AI is powerful but not always necessary. We help you evaluate:

Whether vision or computer interaction is truly required. If the task can be solved with text and APIs, simpler approaches may be more reliable.

Whether accuracy requirements are achievable. We test performance on representative inputs and define acceptable error rates and fallbacks.

Whether the workflow can be made safe. UI automation and document interpretation need guardrails, auditability, and human approval where consequences matter.

Whether the operational trade-offs are acceptable. Multi-modal inputs increase data volume and complexity; we assess whether the payoff is worth it.

Sometimes multi-modal capability is transformative. Sometimes text-based approaches work fine.

Use these prompts to clarify scope and identify where multi-modal capability creates value.

“Which steps in this workflow require seeing (documents/screens), and which can be handled via APIs and structured data?”

“What are the highest-risk failure modes for a computer-use agent here, and what guardrails reduce risk?”