Advancements in Multimodal Agentic AI Systems

Multimodal agentic AI can see, hear, read, and take actions on its own. Think of a support bot that reads your screen, listens to your problem, checks your past chat, then books a fix, without you typing a word. 

For businesses, this means moving beyond basic AI tools that only handle one type of input or simply offer suggestions. This technology enables more efficient interactions, improving customer support, streamlining processes, and enhancing decision-making.

In this blog, you’ll see how it’s changing fast, what’s new in 2025, and how these advancements can actually help your work.

What you need to know:

• Multimodal Capabilities: Multimodal agentic AI can process and act on text, images, voice, and more with minimal human input.
• Advanced Reasoning & Memory: These systems go beyond traditional AI by using memory, real-time feedback, and cross-modal reasoning.
• Future Trends: Expect future agents to collaborate, adapt, and integrate across tools seamlessly.
• Codewave Expertise: Codewave specializes in building custom multimodal agentic AI systems to enhance efficiency and drive smarter decision-making.

How does Multimodal Agentic AI Work?  

You’re on a video call with tech support. Instead of asking you to explain the issue, the assistant looks at your screen, listens to your voice, scans your system log, checks your last ticket, and fixes the problem, on its own. No back-and-forth. No repeated questions. Just a result.

That’s multimodal agentic AI in action.

“Multimodal” means it can handle different inputs, text, images, audio, video, even code. “Agentic” means it can take initiative, use tools, and follow goals. 

You’ve already seen it in action through:

• GPT-4o responding to speech, images, and text in one go
• Gemini 1.5 Pro handling diagrams, documents, and instructions together
• Claude 3 Opus reading long PDFs, understanding context, and summarizing tasks

Most older models could understand different inputs, but that’s where they stopped. You’d feed them a photo, and they’d describe it. Ask a question, and they’d answer. 

Here’s how the difference plays out:

Traditional Multimodal Models	Multimodal Agentic AI
Can label objects in an image	Uses the image to generate a report or suggest fixes
Converts speech to text	Uses speech, checks tools, and performs tasks based on intent
Handles one input mode at a time	Mixes text, visuals, audio, and code seamlessly
Can’t take actions beyond output	Launches tools, edits files, sends messages
No memory or task flow	Builds memory, follows steps, adapts mid-task
Relies on prompt-by-prompt control	Follows goals, not just instructions

Codewave goes further, creating multimodal agentic AI systems that understand inputs and take action across platforms, tools, and data sources.

Tired of AI projects that stall after the prototype phase? Explore Codewave’s Agentic AI Product Design and Development services, where we’ll build systems that think, act, and deliver, start to finish.

To understand what’s changed, it helps to see how all these input types actually come together behind the scenes.

Here’s a breakdown of what powers these systems: 

1. Multimodal Foundation Models

These models process inputs like text, images, audio, video, and code within a shared system.

Architecture: Most use transformer-based setups, with either unified encoders or modular encoders.

• Unified Models: These models process all inputs through a single, shared framework.
• Modular Models: These models handle different input types through specialized processes before merging them in a later stage.

Examples:

• GPT-4o handles voice, vision, and text in real-time with a unified model.
• Gemini 1.5 Pro uses memory and long-context support to analyze documents, diagrams, and code together.

These models are a foundation for more advanced AI systems, enabling them to perform complex tasks like real-time image recognition, voice synthesis, and natural language understanding across different formats.

2. Agentic Planning and Task Execution

This layer gives the model a sense of direction. Instead of just responding, it can take actions, break down goals, and make decisions.

• ReAct (Reasoning + Acting): Alternates between “thinking” and “doing.” Used for tool use and task chaining.
• AutoGPT-style Loops: Recursive prompt-feedback cycles that help the agent generate sub-goals and complete long tasks.
• Plan-and-Execute: Separates high-level planning (deciding what needs to be done) from low-level execution (doing each step).

3. Tool Use and External Actions

To take real action, the AI needs access to tools, APIs, and user interfaces.

• Toolformer: Teaches models when and how to call external tools like calculators, web search, or file systems.
• OpenAI Assistants API, LangChain, AutoGen: Let models interact with apps, browsers, databases, and custom tools.
• Adept ACT-1: Navigates software interfaces like a human, clicking, typing, and interacting with live apps.

4. Memory and Context Handling

Agents need memory to track what they’re doing, what they’ve done, and what the user prefers.

• Short-term memory: Keeps track of information during a task (held in token windows or temporary buffers).
• Long-term memory: Stores past interactions, user preferences, and tool-specific knowledge (via vector databases or persistent memory systems).
• Examples:
  • OpenAI’s memory previews allow models to remember facts across sessions.
  • LangGraph enables branching workflows with persistent state across multiple steps.

5. Cross-Modal Attention and Alignment

To combine inputs like text and images, the model needs to align them correctly.

• Cross-Attention Mechanisms: Let the model link language tokens to visual patches, audio segments, or code blocks.
• Contrastive Learning: Used during training to teach the model how different input types relate, like linking a caption to the correct image (as seen in CLIP or Flamingo).

6. Learning and Feedback Loops

Modern agentic systems adapt based on how users interact with them.

• RLAIF (Reinforcement Learning from AI Feedback): A training method where AI models refine themselves using model-generated feedback instead of human rankings.
• Human-in-the-loop: Used for systems that require precision, such as legal or medical agents, where human corrections guide future responses.
• Online fine-tuning: In-progress research is exploring ways for agents to learn mid-task based on live user feedback.

Dealing with inefficiencies in your AI development process? Check out Codewave’s GenAI Development services, where we’ll help you build custom AI solutions that automate, generate, and adapt at scale.

Now that the basics are clear, let’s look at the breakthroughs that moved multimodal agentic AI. 

What’s Changed? Key Breakthroughs and Innovations

In 2024, GPT-4o responded to live audio, processed images, and held a fluid back-and-forth, all in under 232 milliseconds. That’s faster than a human blink.

Just a year earlier, models needed separate tools for each task, and you’d still have to prompt them step by step. They couldn’t plan, act, or switch between modes without help.

Below are the key breakthroughs and trends driving multimodal agentic AI forward.

Native Tool Use & Action Chaining

Most AI tools used to wait for you to tell them what to do, one prompt at a time. That’s changed. With action chaining, multimodal agentic AI can plan a task, break it into steps, and carry it out without asking for constant input.

Where this is already working:

• Lamini: Builds internal AI assistants that connect to databases, CRMs, and internal APIs to take real action, not just chat.
• HyperWrite’s Personal Assistant: Can book flights, send emails, or create documents by controlling browser tabs and apps.
• Adept’s ACT-1: Interacts with web apps like Google Sheets and Salesforce, navigating the interface and clicking through tasks like a human.

What’s next?

1. Agents that modify tools, not just use them

Future agents won’t just use apps, they’ll change how those apps work. 

For example, the team behind Adept’s Fuyu-Heavy is testing agents that can rewrite internal functions inside spreadsheets or dashboards by editing backend code based on your instructions. You ask for a new sales formula, and it adjusts the macro, not just the cell.

2. Context-aware multitasking across toolchains

Agents are starting to handle multiple tasks in parallel, adjusting steps based on live inputs.

Projects like Project Astra (Google DeepMind) show agents listening to voice commands while scanning live camera feeds, searching files, and drafting responses, all at once. The trend is toward agents that juggle tools in real time based on shifting user goals.

3. Agents that self-recover from failure

Tool-use chains will soon include fallback logic. Instead of halting when a step fails, agents will retry, reroute, or ask for clarification.

LangChain’s upcoming agent monitoring layer can detect when an API fails or a tool returns invalid output, and automatically re-plan the task or alert the user. This makes agents more dependable in real-life use.

4. Cross-agent orchestration inside orgs

Companies are now testing agent swarms, where different agents manage finance, HR, marketing, etc., and coordinate through shared memory or messaging.

Multi-agent workspace trials inside SAP and Notion AI show one agent preparing a report, another formatting it, and a third publishing or sending it, without human prompts after the initial goal.

Real-Time Reasoning + Feedback Loops

Multimodal agentic AI isn’t just processing input and spitting out a response. It’s reacting mid-stream, remembering what just happened, and adjusting its output in the middle of a task.  

Where this is already working: 

• GPT-4o: Streams output as it receives your voice or text, adjusting tone and pace in real time. 
• Claude 3 Opus: Handles multi-turn conversations while holding context over longer exchanges. 
• Project Astra (Google DeepMind): Responds to real-life audio and visual input in a continuous feedback loop, showing early signs of embodied memory.

What’s next?

1. Persistent memory across sessions 

Agents will remember not just the last task, but your preferences, past errors, and context from previous chats. 

Claude’s memory roadmap and OpenAI’s opt-in memory previews show agents learning over time, like remembering how you like your reports formatted or which tools you prefer for scheduling.

2. Mid-task learning through interaction 

Instead of being trained only once, agents will fine-tune responses on the fly based on user feedback. 

Early work in “online learning” and live reinforcement from feedback is being explored in lab models from OpenAI and DeepMind, where models adapt behavior without retraining cycles.

3. Memory handoff across agents and tools 

Expect agents to pass context and feedback between each other or across tools. 

LangGraph’s upcoming context-passing feature allows memory from one agent to be carried into another’s task window, keeping continuity across workstreams.

4. Agents that adjust tone and strategy dynamically 

Future agents will modify how they interact based on emotional cues or pacing shifts. 

Voice-based experiments in Meta’s AudioCraft and NVIDIA’s Riva are pointing toward agents that can pick up on your mood, urgency, or frustration, and respond accordingly, even mid-sentence.

Math, Code, and Multimodal Logic

Multimodal agentic AI is no longer limited to plain text. It can now interpret graphs, understand equations, read handwritten notes, and debug code.

Where this is already working: 

• Gemini 1.5 Pro: Can read plotted charts, understand the underlying data, and answer questions about trends. 
• Claude 3 Opus: Handles long, complex code files and technical documents, making sense of structure and dependencies. 
• SWE-agent (Princeton): Writes, debugs, and improves real software projects by reading code, logs, and context together.

What’s next?

1. Diagram-to-code conversion 

Agents will convert flowcharts, UI wireframes, and architecture diagrams directly into working code. 

Meta’s research around ImageBind and early projects on diagram parsing show agents beginning to turn sketches into structured software components with minimal user input.

2. Reasoning across formats in real time 

Models will handle visual data, code, and natural language in a single step, without switching tools. 

Newer prototypes like OpenAI’s tool-use + code interpreter fusion are being tested for tasks like solving math word problems using both image parsing and symbolic logic.

3. Editable visual reasoning 

Expect agents to suggest changes directly on graphs, charts, or code UIs. 

Tools like Cursor AI and Codeium are already adding early-stage live-editing based on voice or prompts, and upcoming iterations are set to support image + code editing together.

4. Multimodal logic for engineering and research tasks 

Agents will increasingly be used in STEM research, solving equations while referring to experimental diagrams, raw data tables, and prior publications. 

Projects like SciQA and Allen Institute’s Aristo++ are being trained to combine text, visuals, and symbolic math for use in scientific workflows.

Hitting limits with rule-based systems that can’t adapt or predict? Check out Codewave’s AI/ML Development services to build intelligent models that learn from data and make smarter decisions over time.

Try setting up a basic agent using platforms like LangChain or AutoGen, and test how it handles real tasks across text, images, and tools. 

You could also dig into tool-use decision-making with Toolformer-style self-supervision, where models learn when to act without being told. 

Why Choose Codewave for Multimodal Agentic AI Solutions?

Building a multimodal agentic AI system isn’t just about stitching together APIs or calling pre-trained models. It’s about designing systems that can see, listen, reason, and act, all without constant supervision. At Codewave, we help you go beyond standard automation  by creating agentic AI experiences that truly understand context and deliver results.

Our expertise lies in combining large language models with vision, voice, and code interfaces, wrapped in intelligent workflows that can plan, adapt, and respond on their own.

Want to see how agentic AI can work in your setup? Check out our portfolio to see how we’ve helped businesses bring together models, tools, and actions, into one seamless experience.

What You Get with Codewave’s Multimodal Agentic AI Services? 

• 60% improvement in how quickly and smoothly your AI agents are built and deployed, thanks to pre-trained modules, task chaining, and real-time context handling.
• 3x faster delivery cycles so that you can move from idea to working agent in days, not months. 
• Save up to 3 weeks every month by automating repetitive decisions, tool interactions, and data workflows that normally drain team hours. 
• 25% reduction in development costs by using AI-driven logic, reusable agent templates, and minimal human intervention in day-to-day execution.

Our Services Include:

• Agentic AI Consultation: We assess your current workflows and design a roadmap to integrate agentic AI systems that align with your business goals and scale as you grow.
• Custom Agent Design & Development: From idea to execution, we build AI agents that understand multiple inputs, text, images, voice, and act across tools to get real work done.
• AI + Tool Integration: We connect your agents to live data sources, APIs, internal platforms, and third-party tools for seamless execution across systems.
• Actionable Dashboards & Feedback Loops: We build interfaces that track agent performance, visualize decision flows, and let you fine-tune behaviors in real time.

Curious to see what your data is really capable of? Book a free demo with Codewave’s experts and discover how we can turn your data into real results.

FAQs

Q. What are the main benefits of using multimodal agentic AI in business?

A. Multimodal agentic AI improves efficiency and user experience by enabling seamless interactions across multiple channels, such as text, voice, and visuals. This leads to:

• Enhanced customer support: AI chatbots that can analyze text, images, and voice to provide personalized responses.
• Automated decision-making: AI systems that can adapt and act based on multiple data points, improving business outcomes.
• Better user engagement: Through AI-driven personalized recommendations and context-aware responses.

Q. How can multimodal agentic AI improve customer service experiences?

A. By processing a combination of text, voice, and visual cues, multimodal agentic AI allows customer service systems to understand and respond to queries in a more human-like manner. For example, a support bot could analyze an image of a product issue, understand a voice complaint, and review past interactions to quickly resolve the issue without needing human intervention, enhancing speed and accuracy.

Q. How do multimodal agentic AI systems process multiple types of data simultaneously?

A. Multimodal agentic AI systems use advanced models like transformers, which can handle various data types (text, audio, image, etc.) in parallel. They either use unified encoders (processing all inputs through a single model) or modular encoders (handling different input types separately before merging them). This allows the system to make sense of complex, multi-source data and perform tasks like image captioning or speech-to-text in real-time.

Q. How does memory and context handling work in multimodal agentic AI?

A. Multimodal agentic AI systems use memory to track past interactions, context, and preferences, which enables them to make more accurate decisions over time. Short-term memory is used for task-specific information during active processes, while long-term memory stores user preferences or past actions for ongoing personalization. This helps the system improve responses and adapt to evolving needs, much like how humans remember prior interactions to guide future behavior.

Q. Can multimodal agentic AI systems learn and adapt to new tasks autonomously?

A. Yes, multimodal agentic AI can learn from ongoing interactions and adapt to new tasks autonomously. These systems use feedback loops and reinforcement learning to refine their actions over time. For example, an AI that initially assists with basic customer inquiries can, over time, expand its knowledge base, adapt to more complex customer service tasks, and even improve its ability to predict customer preferences without additional programming.