Coresource provides a managed platform that runs long‑horizon autonomous agents, handling execution, governance, observability, tooling, and self‑improvement automatically. Users specify a high‑level objective, and the platform’s AlphaZero‑style Monte Carlo tree search over a learned world model plans, simulates, and prunes possible paths to execute multi‑day tasks without human intervention. The system supports massive context windows—up to 1.5 billion tokens—and tracks thousands of lines of code across continuous, self‑directed revisions.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Enterprise Software
Funding
Founders
Product
Problem
Software development pipelines struggle to enforce complex, judgment-based standards such as security, architecture, and regulatory compliance because existing checks are limited to mechanical, pre‑specified rules, leaving critical decisions to human reviewers. This gap leads to inconsistent enforcement, missed violations, and increased manual effort.
Solution
Coresource provides a managed runtime that executes long‑horizon autonomous agents capable of interpreting and applying comprehensive standards without human intervention. The platform uses an AlphaZero‑style Monte Carlo tree search over a learned world model to decompose objectives, simulate possible execution paths, prune irrelevant branches, and commit to the optimal plan. Agents run continuously for up to 50 hours, processing billions of tokens of context while maintaining coherence and self‑improvement. The system supplies built‑in governance, observability, and audit trails, ensuring each decision is traceable and can be escalated to a human when necessary. By integrating via an SDK or direct platform access, teams can embed reliable AI agents into CI pipelines or other workflows, turning complex policy enforcement into an automated, trustworthy check.
Target Audience
Primary customers are engineering and security teams that need automated, reliable enforcement of comprehensive standards within CI/CD pipelines, as well as enterprises requiring continuous compliance verification for regulated software deployments.
Features
- AlphaZero‑style Monte Carlo tree search over a learned world model for objective decomposition and optimal path selection
- Managed execution environment with built‑in governance, observability, and durable audit logs for every agent run
- Self‑improvement loop that revises plans mid‑flight, supporting up to 201 autonomous plan revisions in a single mission
- Scalable context handling: processes ~1.5 billion tokens per run while preserving coherence hour after hour
- SDK and platform integration allowing agents to act as CI steps, returning pass/fail/needs‑human outcomes with structured evidence
- Automatic escalation and checkpointing so agents can pause for human input without losing state or consuming excess tokens
- Support for complex, conjunctive standards (e.g., FedRAMP, NIST 800‑53) with exhaustive rule coverage and compliance verification