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Coding Agents
Tier 4
SWE-agent
Summary
The Princeton research agent that introduced the Agent-Computer Interface (ACI) concept: carefully designed commands, file viewers, and feedback formats that dramatically improve LLM performance on real GitHub issues (SWE-bench).
Key Takeaways
- ACI design (commands + viewers + feedback) is a measurable performance lever
- Constrained, purpose-built tools beat raw shell access
- Benchmark-driven iteration (SWE-bench) keeps harness claims honest
Reliability Note
Official repository of a peer-reviewed research project.
Flows informed by this source
2Agent Architecture
Design the Agent-Computer Interface (ACI)
Apply SWE-agent's key finding: agents perform dramatically better when their commands, viewers, and feedback are designed for models, not humans.
01Audit your current tool surface
02Build the windowed file viewer
03Add validated edits with corrective feedback
+2 more steps to Done
Best forproduction-grade builds with strict verification
5 steps90-120 minutesAdvanced
Harness Engineering
Sandbox Untrusted Code Execution
Contain what your agent runs: isolated execution environments with resource limits, network policy, and workspace mounting done right.
01Choose the isolation and define the policy
02Build the sandboxed executor
03Enforce and verify the network policy
+1 more steps to Done
Best forproduction-grade builds with strict verification
4 steps120-180 minutesAdvanced