Dexto
Dexto provides a multi-agent harness that lets engineering teams orchestrate AI agents to investigate complex technical problems in parallel. The platform gives each agent its own isolated cloud computer and shared memory, returning evidence trails with branches that engineers can challenge before making decisions. It is designed for AI-native companies running agent workforces across launch, growth, support, and hiring.
- AI Agents
- Artificial Intelligence
- Developer Tools
- Software Only
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Engineering teams face complex technical problems that span multiple codebases, runtime environments, and deployment layers, making root-cause analysis costly and slow. The expensive part is not typing a patch—it is finding out which patch deserves to exist, as each plausible fix requires its own setup, measurement, and review, creating bottlenecks around scarce engineering attention.
Solution
Dexto provides an operating system for agentic work that lets teams orchestrate and manage AI agents executing complex, multi-step tasks. The platform centralizes agent workflows, tool integration, and oversight, enabling organizations to deploy autonomous processes with control and visibility. In practice, Dexto lets teams dispatch multiple bounded investigations in parallel—agents inspect code, profile workloads, try changes on separate branches, and bring back results an engineer can challenge. Teams define success criteria up front and retain architectural decision-making, while agents expand the search space and people decide what counts as proof. Each investigation returns with a branch and an evidence trail including commands, measurements, functional checks, and a disposition, allowing reproducible results without endless handoffs. Dexto also provides each agent its own cloud computer and a shared system of record, so work continues without human typing and context is not scattered.
Target Audience
Dexto serves engineering teams and technical organizations that need to parallelize complex debugging, performance optimization, and architectural investigation work using AI agents while maintaining human control over decisions. It also targets AI-native companies running agent workforces for launch, growth, support, and hiring functions.
Features
- Parallel agent orchestration that runs multiple bounded investigations simultaneously across code, profiling, CI, and deployment domains
- Evidence-trail capture including commands, measurements, functional checks, and disposition for every agent investigation
- Human-in-the-loop oversight model where teams define success criteria up front and retain architectural decisions
- Centralized agent workflow management with tool integration across engineering domains
- Benchmark and measurement tooling to validate agent results, catching flawed comparisons, missing telemetry, or inconsistent sampling
- Runtime architecture support for isolating agent execution into separate worker pools with independent scaling and monitoring
- Persistent cloud computer per agent with shared memory across all agents and projects
- 50+ integrations and custom MCP connections for extending agent capabilities