Products for Agents
Explore inAi's product category for software, tools, interfaces, workflows, and operating surfaces that AI agents can use.
Explore Products for AgentsProducts for agents / Thesis
Software for the agent era
Why AI agents need products built for them.
Agents are becoming software users.
Most software was built for people looking at screens. AI agents use software differently. They do not need prettier dashboards as much as they need tools they can call, instructions they can understand, state they can preserve, outputs they can inspect, and workflows they can continue.
Software for the agent era is inAi's thesis for this shift. As agents become software operators, products will need to become legible and usable not only to humans, but also to the systems acting for them. That does not mean inAi sells agents. It means we build for a world where agents need software designed around their way of working.
For decades, software assumed a human operator. The user read the interface, understood the page, clicked the button, copied the data, checked the result, remembered the goal, and decided what to do next.
AI agents change that assumption. An agent can pursue a task across multiple steps, call tools, inspect outputs, pass structured information, continue work, and ask for review when needed. It may still act under a human or organizational goal, but the immediate operator is no longer always a person clicking through a screen.
That creates a new design problem: products must become usable by agents, not only visible to humans.
Agents are more than chatbots.
A chatbot answers in a conversation. A script follows fixed instructions. A traditional automation runs a predefined workflow.
An AI agent is different because it can combine language understanding, planning, tool use, state, and feedback across a task. It can decide which step comes next, call a capability, inspect what happened, recover from errors, and continue until the work reaches a usable state.
That does not make every agent general intelligence. It does not make every agent safe, reliable, or useful. But it does change what software has to expose. If agents are expected to operate products, the products need to describe themselves, expose capabilities, preserve state, and return results in forms the agent can use.
Human-first software is not enough for agent-operated work.
Human-first software depends on human perception. A person can scan a dashboard, understand a visual layout, infer what a vague button means, remember a previous step, and adapt when a workflow changes.
Agents need different assumptions. They need explicit capabilities. They need structured inputs and outputs. They need documentation written in a form they can parse and follow. They need state when work continues over time. They need recovery paths when a call fails. They need permission boundaries when an action could affect a file, account, customer, repository, catalog, or external system.
The agent era does not remove human interfaces. People will still need screens, dashboards, approvals, and explanations. But software will increasingly need a second surface: one built for agents to operate.
Agent-ready software needs more than an API.
An API can be part of agent-ready software. It gives software a way to expose actions and data beyond a visual interface. But an API alone does not tell an agent when to use a tool, what the result means, how to recover when something fails, what state should be preserved, what actions require approval, or how a workflow continues after the first call.
Agents need a product surface, not only an endpoint. They need the contract, the instructions, the examples, the expected inputs, the shape of the outputs, the state model, the failure modes, the permission boundaries, and the review logic where the task requires trust.
That is why agent-ready software is not simply API documentation with a new label. It is product design for systems that can read, call, inspect, remember, and continue.
What agent-ready software requires
There is no single universal checklist that makes software agent-ready. The right design depends on the task, the risk, the user, and the system around the agent. But several requirements appear again and again.
Not every small integration is a product. Some things are components, schemas, surfaces, or infrastructure. A product for agents should provide a meaningful capability that an agent can use end to end as part of real work.
- Clear capabilities: An agent needs to know what the product can do. Capabilities should be explicit, named, bounded, and described in a way an agent can use. Vague interface labels are not enough.
- Callable tools and actions: Agent-ready products expose actions the agent can call: functions, commands, tool surfaces, APIs, MCP-style interfaces, or other structured action layers. The agent should not have to guess through a screen when a clearer tool surface can exist.
- Structured inputs and outputs: Agents work better when inputs and outputs are predictable. Schemas, typed fields, stable formats, and clear error messages reduce ambiguity and make workflows easier to inspect, repeat, and recover.
- Agent-readable documentation: Human documentation explains a product to a person. Agent-readable documentation explains capabilities, constraints, examples, workflows, edge cases, and expected results in a form language models and agents can use during task execution.
- State and memory: Many real tasks do not finish in one call. Agents need state: what happened before, what files were touched, what decisions were made, what remains unresolved, what context should be carried forward, and where work can resume.
- Recovery paths: Agent-facing products need to explain what happens when something fails. A useful system should let the agent understand the failure, retry safely, ask for review, or continue through another route instead of collapsing into a vague error.
- Permissions and review: Not every action should be free. Some tasks can run with high autonomy. Others require approval, scopes, sandboxing, review, or human confirmation. Agent-ready software should make those boundaries explicit instead of hiding them behind vague trust language.
- Logs, summaries, and inspection: When an agent acts, people and systems may need to inspect what happened. Logs, summaries, diffs, traces, or result explanations can make agent-operated work more understandable without turning every step into a manual process.
- Reusable workflows: A product for agents should support repeated use, not only one-off calls. Agents need workflows they can reuse, compose, continue, and adapt under human or organizational goals.
Documentation becomes part of the interface.
In human-first software, documentation often sits outside the product. People read it before or after using the system.
For agents, documentation can become part of the operating surface. The agent may need to read instructions during the task, understand examples, decide which tool to call, follow constraints, interpret outputs, and recover from failures. That means documentation cannot be only marketing copy or a generic developer reference. It has to describe the product in a way that supports action.
This is why emerging conventions such as machine-readable API descriptions, agent-readable documentation, and structured website summaries matter. They point toward a web and software ecosystem where products increasingly explain themselves to systems as well as to people.
First humans choose tools for agents. Later agents may discover tools themselves.
The first market for agent-ready software is still human-led. Developers, teams, companies, and power users choose tools, configure them, give them to agents, and supervise the results.
That is already enough to change product design. If a human is choosing tools for an agent, the product has to explain not only why a person should care, but what the agent can actually do with it.
Over time, agents may become better at discovering, comparing, and selecting tools under human or organizational goals. That is a future-facing direction, not a claim that autonomous agents already buy or select inAi products today. But it changes the long-term design problem: products should become legible to agents, not only attractive to humans.
Why this matters for inAi
inAi is an AI-native product company. We build for business, consumers, agents, and open ecosystems because AI is becoming a new operating layer for software and work.
Products for Agents is the part of that architecture focused on the software agents will use. It asks a specific question: if agents become real operators inside workflows, what products, tools, memory, documentation, code systems, and interfaces will they need?
That question connects directly to our broader mission. Bringing AI to the real world is not only about placing models behind chat windows. It is about building the products and systems that let intelligence act usefully in real contexts.
codex-mcp-wrapper
Open Source / Active
Agent-era developer tooling
`codex-mcp-wrapper` is the clearest current public cross-link between inAi's Open Source portfolio and the Products for Agents direction.
It belongs to Open Source first. It should not be treated as the whole Products for Agents category. But it points toward the kind of callable, tool-oriented infrastructure that matters when agents interact with code, repositories, tasks, and review flows.
Memory, code, and stateful work for agents
Beyond public Open Source work, inAi is exploring internal tools around agent memory and agent-oriented code workflows. These systems are not being presented as public products yet. They are internal and future-facing work: part of how we study what agents need when they inspect, modify, reuse, and continue work over time.
The direction is clear even before every product is public. Agents need memory. They need state. They need code tools designed for their way of operating. They need software that helps them continue work without losing context at every step.
Why this connects to AGI as a System
inAi's stance is that general intelligence may be more likely to emerge from systems than from one isolated model: models, agents, tools, memory, perception, execution, coordination, feedback, and environment working together.
Software for the agent era is the product-layer consequence of that stance. If agents become part of intelligent systems, they need tools they can use, memory they can rely on, instructions they can understand, and workflows they can operate.
The model matters. But the system around the model also matters. Products for Agents is where inAi builds for that system layer.
A product category informed by research
Agent-ready software depends on questions that are still developing: how agents decide, when they should ask for review, how they preserve context, how they use tools, how they recover from mistakes, and how they coordinate across longer work.
That is why this page connects naturally to inAi's Research layer, especially work on agentic decision systems, limits of intelligence, AI for knowledge creation, and AI in business operations. Research helps us understand the systems. Products turn the useful parts into software.
Agent-facing tools need clear boundaries.
When agents operate software, trust is not only a legal or security topic. It becomes a product-design question.
What can the agent call? What state does it keep? What result does it return? What can it change? What actions require review? What should be reversible? Where should the workflow be free enough to create value, and where should control create trust?
inAi does not believe every AI product needs the same control model. Agent-facing software should be open where openness creates value and controlled where control creates trust. The right design depends on the task, the stakes, and the user or organization behind the agent.
What this page does not claim
Software for the agent era is a product thesis, not an AGI claim.
inAi does not claim to have AGI. We do not claim that Products for Agents proves AGI. We do not claim that PageMind or Emplo are agent products by default. We do not claim that agents already autonomously discover and buy inAi tools.
We also do not expose private internal architecture on this page. The public idea is simple enough: as agents become software operators, they will need software designed for how they work.
Related paths
Build for the systems that will use software next.
The agent era will not arrive only through smarter models. It will also arrive through better products: tools, interfaces, workflows, documentation, memory, permissions, and software surfaces that make agents useful inside real work.
That is what inAi means by Products for Agents. We are building for a world where software is used by people, companies, builders — and increasingly by the agents acting for them.