For the last few years, we’ve seen AI move from the periphery of business strategy to its very core.
According to IDC, global spending on AI-centric systems is projected to exceed $300 billion by 2026.
And one of the biggest shifts happening right now is in how businesses build and deploy intelligent agents. Not just models that generate text, but systems that can take real action.
We’re entering a new era where AI agents can do meaningful work on your behalf: booking, searching, analyzing, deciding.
OpenAI’s new tools for 2025 are a big step in that direction. Building AI agents that can search the web, interact with files, use software, and complete multi-step workflows is no longer theoretical. It’s accessible.
At Bitcot, we believe this changes everything. These tools unlock a new class of applications where AI doesn’t just assist people, it helps run the process.
In this article, we’ll walk through what these tools are, how to use them, and what this means for the next generation of digital products.
AI Just Got a Lot More Powerful
Automation has changed.
What was once limited to basic, repetitive functions is now evolving into intelligent, autonomous systems that can handle far more complex workflows.
In March 2025, OpenAI introduced a comprehensive suite of tools aimed at simplifying the development of AI agents – systems designed to autonomously perform tasks on behalf of users.
These tools include the Responses API, built-in functionalities like web and file search, computer use capabilities, and the Agents SDK.
Together, they provide developers with the necessary components to build, deploy, and manage intelligent agents effectively.
They give developers the foundation to build agents that don’t just understand language, but actually use tools, navigate interfaces, and complete real-world tasks.
This shift opens up exciting possibilities for industries ranging from customer service to data processing and beyond. By empowering AI agents to handle complex, multi-step processes, businesses can achieve higher levels of efficiency and innovation.
Paul Baier, CEO of GAI Insights, noted that these tools are especially valuable for enterprises and developers who don’t want to spend time building agentic tools from scratch and are looking for more support than open-source solutions typically offer. He explained that these building blocks are easier to use, more powerful, and will likely be embraced by developers because they save time.
If you haven’t seen it yet, this OpenAI demo gives a great look at how these tools actually work in action. It’s well worth 5 minutes.
What is the Responses API?
At the heart of OpenAI’s new agent-building tools is the Responses API, a powerful upgrade that combines the simplicity of Chat Completions with the tool-using capabilities of the Assistants API. Think of it as the brain that powers your agent, now with hands it can use to get things done.
Unlike previous APIs that focused solely on text output, the Responses API is built for action. It gives your agents the ability to respond intelligently, use tools like web search or file lookup, and even trigger multi-step tasks, all from a single interface.
For businesses, this means you can now build agents that are not just conversational, but operational. Whether it’s pulling data from a knowledge base, answering customer questions using up-to-date web content, or completing internal workflows, the Responses API makes it possible without requiring custom integrations from scratch.
Key Capabilities
- Tool Awareness: The API can automatically determine when and how to use connected tools like web search or file analysis.
- Streaming Output: Get partial responses as they’re being generated for faster, more responsive UX.
- Unified Interface: Instead of juggling multiple APIs for different tasks, Responses gives you one endpoint to rule them all.
- Built-in Tracing and Debugging: Easily track how your agent reached a decision or action, making testing and iteration smoother.
- Data Isolation: Business data is kept separate from model training by default, supporting privacy and security at scale.
Also Read: How to Build an AI System in 7 Steps – Your 2025 Roadmap
At Bitcot, we see this API as the new foundation for AI-driven products. It’s not just about chatting with users anymore, it’s about empowering AI to work alongside your team, your users, and your systems.
What is the Assistants API?
The Assistants API is a framework from OpenAI that allows developers to create customizable, persistent AI agents with memory, tool usage, and file support, all in a more structured way than raw Chat Completions. It’s designed to give your AI assistants the ability to understand context over time, use tools like code interpreters or file retrieval, and operate with consistent behavior across sessions.
Unlike typical chat interfaces that forget everything after a session ends, the Assistants API supports persistent threads, allowing your agent to “remember” past interactions and build more coherent, ongoing experiences. This makes it ideal for use cases like customer support, coding help, document Q&A, or task automation.
Key Capabilities
- Threaded Conversations: Maintain continuity between user sessions, making the assistant feel more personal and context-aware.
- Tool Integration: Seamlessly call tools like code interpreters, retrieval systems, or custom APIs to extend your assistant’s functionality.
- File Handling: Upload and interact with documents directly. Your assistant can read and respond based on the file contents.
- Memory (coming soon for all users): Remember important facts, preferences, or prior decisions to improve interactions over time.
In essence, the Assistants API is your go-to if you’re building an AI with structure, memory, and interactivity. The Responses API builds on this foundation, making it more streamlined and action-ready, ideal for real-time, tool-using agents in production environments.
What’s the Difference Between the Responses API and the Assistants API?
OpenAI gives you two options for building AI agents: the Responses API and the Assistants API. While they sound similar, they’re made for different things.
Think of it like this:
- Responses API is like a fast-thinking assistant that can take actions right away.
- Assistants API is more like a long-term teammate that remembers things and helps over time.
If you’re still confused, don’t worry. We’ll break down the differences so you can pick the right one for what you’re building.
| Feature / Capability | Responses API | Assistants API |
| Primary Focus | Fast, real-time responses with tool use | Structured, persistent assistants with memory and tools |
| Use Case | Production-ready agents that act and respond instantly | Stateful assistants that manage ongoing conversations |
| Thread Management | Stateless or lightweight thread usage | Persistent thread support with full conversation history |
| Tool Usage | Dynamic tool calling at runtime | Pre-configured tools per assistant instance |
| Streaming Output | Yes real-time partial response streaming | Yes but tied to the thread interaction model |
| File Support | Yes it supports file uploads for agents | Yes assistants can access uploaded files per thread |
| Customization | Built for plug-and-play with custom tools or workflows | Built for deep assistant configuration and role assignment |
| Ideal For | Real-time agents, chatbots, customer support tools | Personal assistants, long-term projects, tutoring bots |
| Simplicity vs Structure | Prioritizes simplicity and flexibility | Prioritizes structure and consistency |
In a nutshell:
- Use Responses API when you want a flexible, real-time agent that can take actions, call tools, and get things done fast.
- Use Assistants API when you need a more persistent, memory-enabled assistant with a clear role and long-term context.
Why You Should Use Built-in Tools to Give Your Agent Superpowers
An AI agent is only as useful as the tools it can access, and OpenAI now gives you some serious firepower right out of the box.
With built-in tools like Web Search, File Search, and Computer Use, your agents can interact with the real world in ways that go far beyond answering questions.
These tools transform passive assistants into proactive problem-solvers.
1. Web Search
Your agent can now tap into the live internet to find real-time information with citations. No need to rely on static knowledge or outdated models. Whether it’s pulling the latest stock prices, researching competitors, or finding reviews, agents can now do what humans do: search, read, and respond.
Use Case: A travel assistant that checks live flight prices and weather updates before suggesting an itinerary.
2. File Search
With File Search, your agent can quickly navigate large document sets like PDFs, Word files, and more to extract the right information at the right time. Built-in features like metadata filters and ranking make it fast and relevant.
Use Case: A customer support agent that pulls exact refund policies from your internal documentation while chatting with a customer.
3. Computer Use
This is where it gets exciting. Agents can now simulate mouse clicks, keyboard inputs, and even browser navigation. Essentially, they can use a computer like a human, but faster and more reliably.
Use Case: An AI assistant that logs into vendor portals, downloads invoices, and updates your internal systems automatically.
Together, these tools unlock something much bigger than automation. They let your AI agents operate like skilled teammates, gathering data, taking action, and delivering results without needing to escalate to a human.
How You Can Orchestrate Complex Workflows Using the Agents SDK
While the Responses API is the brain, and the built-in tools are the hands, the Agents SDK is what ties it all together. It gives you the framework to design, configure, and run autonomous agents that can reason, plan, and act, based on your unique business logic.
The SDK doesn’t just let you call tools. It lets you build an agent loop: a system that evaluates its own progress toward a goal, decides on next steps, and adjusts in real time.
That’s a major leap forward from traditional scripted automation.
What Makes the Agents SDK Special?
- Stateful Agents: Your agent can remember what it’s doing, keep track of goals, and work through multi-step processes.
- Custom Toolchains: Easily define your own tools or combine OpenAI’s built-in ones with proprietary systems.
- Goal-Oriented Design: Agents are designed to achieve outcomes, not just respond to inputs.
- Fine-Tuned Control: You decide how often the agent loops, how it handles failures, and how it adapts mid-task.
Real-World Use Case:
Imagine an agent designed to onboard new employees. It can:
- Pull offer letter templates from a file system.
- Generate personalized welcome emails.
- Log into an HR portal to set up accounts.
- Schedule orientation meetings automatically.
With the Agents SDK, all of this becomes a structured loop, driven by goals, guided by tools, and executed without manual oversight.
At Bitcot, a leading AI automation agency, we’re excited by what this unlocks for our clients: agents that don’t just assist—they own processes. Whether it’s customer service, internal operations, or product experiences, the SDK gives you the architecture to build agents that work like autonomous teammates.
Also Read: How AI Workflow Automation Can Modernize Your Business in 2025
Our team recently worked with a logistics company to rebuild how their internal dispatching worked. No more manual steps, no more bottlenecks. We used the Agents SDK to design an agent that could pull delivery schedules, assign drivers, update dashboards, and even flag issues in real time.
It was one of those moments where everything clicked: the right tools, used the right way, completely changed how the business ran. What used to take a team half a day now happens in minutes.
That’s the power of this shift, it’s not just efficiency gains. It’s building a future where software doesn’t just support your team, it is part of the team.
What Happens When Agents Analyze Image Inputs
For AI to truly operate autonomously in the real world, it needs to go beyond just text. It needs to see, understand, and interpret the environment around it, just like a human would.
Since OpenAI took a significant step forward by introducing image input capabilities for its AI agents, this is a game-changer.
Imagine an AI agent that can not only read and respond to written commands but also analyze and process images in real-time. Whether it’s scanning a photo, interpreting a screenshot, or reading a scanned document, these agents are now able to extract meaningful insights from visual data.
This gives AI agents a much deeper understanding of the world around them and opens up new possibilities for how they can interact with users and systems.
This capability allows agents to:
- Interpret images: AI agents can now process and analyze photographs, screenshots, diagrams, and other visual data to extract useful information.
- Enhance decision-making: By including visual data in their decision-making process, agents become more informed and capable of making better, context-aware decisions.
- Complete real-world tasks: From reading receipts and invoices to identifying objects in photos, agents can handle a broader range of real-world tasks that involve images.
The addition of image analysis doesn’t just enhance an agent’s ability to perform tasks. It fundamentally changes the way we think about automation.
With this new capability, developers can build agents that are not just reactive but proactive, able to see and act in ways that weren’t possible before.
How You Can Extend Your Agent’s Functionality Using Custom Tools
As powerful as the built-in tools are, the real potential of AI agents comes when developers can extend their capabilities. In the same way that humans rely on specialized tools to enhance their own abilities, AI agents can be empowered by integrating custom tools designed for specific tasks.
This is where custom tools come in, giving developers the flexibility to tailor agents to solve unique problems and meet the specific needs of their business or industry.
It’s like giving your AI agent a toolbox with custom-fitted instruments. The default tools are like a basic screwdriver set. They’re useful, but limited. Custom tools are the specialized wrenches, precision blades, or diagnostic scanners that let your agent work on the unique machinery of your business. Suddenly, it’s not just capable, it’s built for the job.
By adding custom tools, you can significantly enhance an agent’s functionality, allowing it to interact with proprietary systems, access specialized data, or perform highly specific operations.
Also Read: Best AI, Low-Code, and No-Code Business Tools in 2025
Imagine an agent built to automate the process of ordering inventory in a warehouse. Out of the box, the agent might be able to understand text-based requests, search for product details, and even make purchase recommendations.
But what if you could take it further?
By adding a custom tool, the agent could now interface with your internal inventory management system, pulling real-time stock data, or even triggering automated restocks when supplies fall below a certain threshold.
Custom tools open up a world of possibilities. They allow your agents to:
- Integrate with proprietary systems: By connecting to internal databases, APIs, or legacy systems, custom tools allow agents to access and interact with data that isn’t readily available through standard APIs.
- Perform specialized tasks: Whether it’s running industry-specific algorithms, managing complex workflows, or processing unique file types, custom tools enable agents to handle complex operations that require specialized knowledge or technology.
- Enhance decision-making: Custom tools can provide agents with additional context, enabling them to make better-informed decisions, even in highly specialized fields like healthcare, finance, or manufacturing.
The power of custom tools lies in their ability to extend the functionality of AI agents far beyond basic automation. By building your own tools and integrating them seamlessly with agents, you unlock the potential for real-time, intelligent decision-making that’s perfectly tailored to your needs.
This is about creating smarter, more capable AI agents that can take on complex, nuanced responsibilities in any environment.
How You Can Ensure Your AI Delivers Fast, Responsive Experiences
As AI technology advances, the expectation for lightning-fast, real-time interactions has never been higher. Users expect instant responses, seamless experiences, and uninterrupted workflows. This is where the true power of AI agents comes into play.
To deliver top-tier AI performance, optimizing for speed and efficiency is paramount. When developing AI agents, ensuring quick response times and smooth operation isn’t just about providing a good user experience; it’s about creating a solution that’s reliable, scalable, and capable of handling high demands.
There are several ways to achieve this:
- Optimizing API calls: By minimizing the number of calls needed and maximizing the data processing power of each request, agents can work faster and more efficiently. Reducing latency is key to creating smooth, responsive interactions.
- Efficient data processing: Implementing optimized algorithms and data handling techniques can dramatically improve the speed at which an agent processes requests, making the whole system more agile and responsive.
- Using caching mechanisms: Caching frequently used data can reduce load times and allow the agent to respond faster by retrieving data that is already available, rather than querying external sources every time.
- Parallel processing and microservices: Breaking tasks into smaller, independent processes allows agents to work concurrently, significantly reducing the time it takes to complete complex workflows.
Blazing-fast AI isn’t just about performance. It’s about ensuring that the agents remain agile under pressure. Whether you’re scaling to serve thousands of users or handling heavy workloads, ensuring high-speed performance across the board makes all the difference in keeping users satisfied and systems running smoothly.
Also Read: How Non-Technical Teams Can Start Their AI Journey with Discovery
The best part? By focusing on performance, you don’t just improve speed, you also improve reliability. A fast AI agent that can handle large amounts of data and requests without slowing down is an agent that can be trusted in mission-critical environments.
In a world where every second counts, delivering fast, intelligent experiences is essential. And with the right tools and optimizations, your AI agents can be the high-performance engines businesses need to win.
Where Do You Start When Building an Agent: A Step-by-Step Guide
With the right tools and a little guidance, anyone from startups to enterprise teams can build agents that actually do things. Not just talk, but act. Here’s how you get started.
Now let’s walk you through how to start building with these tools, and why this moment matters. This is the foundational loop for building a functional agent in 2025:
1. Define the Use Case
Start with a clear understanding of what your agent is supposed to do. Is it handling customer inquiries? Processing invoices? Running daily business operations? Great agents begin with great specificity. Focus on value: what pain point are you solving?
2. Design the Agent’s Core Behavior
Use the Responses API to define how your agent should respond to user inputs. This is where your agent learns to communicate clearly, follow instructions, and stay grounded in the purpose you defined. Responses form the backbone of the agent’s behavior.
3. Equip It with the Right Tools
Out of the box, agents have access to built-in tools like code interpreter, web search, and file browsing. But you can also integrate custom tools like APIs, internal systems, databases to give your agent capabilities unique to your domain.
4. Add Perception: Image & File Inputs
To make your agent more aware of the world, add the ability to process image inputs. This is especially useful in workflows where visual data plays a key role like validating documents, interpreting screenshots, or recognizing physical products.
5. Use the Agents SDK for Workflow Management
For agents to go beyond one-off tasks, they need memory, planning, and orchestration. That’s where the Agents SDK comes in. It allows your agent to string together multiple steps, use tools conditionally, and respond in context over time.
6. Optimize for Speed & Scalability
Build with performance in mind from the start. Caching, smart routing, and scalable architecture will keep your agent fast and responsive, even as demand grows.
7. Deploy, Test, Improve
Ship early. Test often. Use real feedback to iterate. The best agents evolve over time, learning from both their environment and your users.
This is the new blueprint for automation. For building AI that doesn’t just understand what you say, but gets things done. If you’re building for the future, this is your starting line.
Where Agents Are Headed Next
What we’re seeing in 2025 is just the beginning. OpenAI’s new agent tools represent a major leap, but even bigger changes are coming. The infrastructure is evolving fast, and soon, AI agents won’t just assist with isolated tasks. They’ll manage entire systems, learn from experience, and collaborate with both humans and other agents.
What’s on the Horizon?
- More Autonomy, Less Micromanagement: Future agents will be able to handle complex decision-making with minimal oversight, thanks to better reasoning loops, memory, and long-term planning.
- Seamless Tool Integration: Expect tighter interoperability between OpenAI tools and enterprise software like CRMs, ERPs, and proprietary internal systems so agents can plug into your actual workflows with zero friction.
- Multi-Agent Collaboration: Agents won’t work alone. OpenAI is exploring ways for multiple agents to collaborate in shared environments, handling tasks in parallel, solving problems collectively, and passing off work intelligently.
- Continuous Learning Loops: Imagine agents that get better over time, adjusting based on past performance, user feedback, or changing business needs. That future is approaching fast.
At Bitcot, we’re not just watching this unfold, we’re helping shape it. The opportunity for businesses is clear: those who start experimenting with agents today will be the ones leading tomorrow.
The groundwork is here. The tools are here. The next generation of AI-powered products starts now.
Final Thoughts
We’re standing at the edge of a new era where AI isn’t just a tool in automation. It’s rewriting the entire playbook.
With OpenAI’s tools for building AI agents, that future is now within reach. They give us the building blocks to move beyond static automation and into a world of intelligent, goal-driven systems.
For startups, enterprises, and product teams alike, this is a chance to rethink how work gets done. Agents powered by the Responses API, built-in tools, and the Agents SDK can navigate complex tasks, use real-time data, and adapt to your business in ways that were impossible just a year ago.
The ones who get this will win.
At Bitcot, we see this as a turning point for businesses ready to move from automation to autonomy. Whether you’re automating internal ops, creating smarter customer experiences, or inventing entirely new AI-powered apps, we can help. Reach out to us here.
Let’s build agents that do more than assist. Let’s build agents that drive progress.
