01
Excessive round trips
Agents repeatedly reason, call one operation, wait, and then reason again.
SoftwareAgents
The agent interface for your software
Make your software work for agents.
Charon builds the semantic interfaces that let ChatGPT, OpenClaw, and other AI agents understand, plan, and operate your product quickly and reliably.
Built on top of your existing APIs, SDKs, and internal capabilities.
Your API was built for developers. Agents use it differently.
Traditional APIs describe endpoints and data structures. Agents need to understand what operations accomplish, how they compose, what can go wrong, and whether the intended outcome was actually achieved.
02
Poor semantic context
Schemas describe valid syntax without fully expressing intent, dependencies, or side effects.
03
Rigid tool surfaces
Fixed wrappers can hide useful capabilities and restrict how an agent solves a task.
04
No outcome verification
A successful API response does not prove that the user’s objective was completed correctly.
Canva interface benchmark
Same model. Same prompt. Different interface.
We are comparing agents operating Canva through its native MCP tools against agents using Canva’s programmable Design Editing API directly.
Benchmark in progress
Precise infographic recreation
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- Task success
Task success
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- Execution time
Execution time
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- Model round trips
Model round trips
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- Human corrections
Human corrections
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Complex multi-element edit
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- Task success
Task success
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- Execution time
Execution time
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- Model round trips
Model round trips
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- Human corrections
Human corrections
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Layout and consistency repair
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- Task success
Task success
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- Execution time
Execution time
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- Model round trips
Model round trips
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- Human corrections
Human corrections
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What we are measuring
- Complete task success
- Visual and informational accuracy
- Execution time
- Model round trips
- Human corrections required
Results will be published after repeated controlled runs. No cherry-picked single examples.
One semantic control layer. Every major agent.
Charon turns the capabilities already inside your software into an interface that agents can discover, compose, execute, and verify.
Discover
Map the relevant operations, constraints, permissions, and dependencies.
Plan
Let the agent choose and compose the capabilities needed for the objective.
Execute
Validate and batch operations close to the underlying software.
Verify
Inspect the resulting state and confirm that the intended outcome was achieved.
Integrate once. Support agents everywhere.
The underlying semantic model and execution layer remain shared. Charon adapts discovery, authentication, permissions, and responses to each agent environment.
- ChatGPT
- OpenClaw
- Codex
- Claude
- Local agents
- Future agent clients
Make your product agent-ready.
We are looking for a small number of software companies with existing APIs or SDKs to test Charon against real customer workflows.
Apply as a design partner
Best suited to SaaS products with complex, multi-step workflows and an existing API or SDK.
Design partners receive
- A benchmark of important workflows through their current agent interface.
- An analysis of capability gaps, unnecessary round trips, and failure modes.
- A proposed semantic interface designed around measurable task completion.
- Compatibility testing across ChatGPT and local agent environments.