Prove your agents are getting better
Evaluation design for real software workflows: compare agent behavior with and without context, track failure modes, and keep the system honest as models and tools change.
What this covers
Practical, source-backed work that improves how your team uses AI tools.
Task-based evals
Use real developer tasks instead of abstract benchmarks.
- Bugfix tasks
- PR review tasks
- Incident response tasks
Failure-mode tracking
Measure the mistakes that matter in engineering workflows.
- Wrong assumptions
- Stale-doc reliance
- Unsafe action proposals
With-context vs no-context
Show whether the context layer actually improves outcomes.
- Time-to-answer
- File-reading waste
- PR quality comparison
Regression scorecards
Keep evals useful as Claude, Cursor, Codex, Copilot, and internal tools change.
- Model comparison
- Context quality drift
- Release checklists
Engagement flow
Start narrow, prove value, then expand permissions and automation carefully.
Select tasks
Choose representative workflows and failure modes.
Run baseline
Measure current agent behavior and context gaps.
Compare
Track improvements after context system changes.
Make agent reliability measurable.
A better context system should show up in fewer wrong assumptions, better PRs, and safer operations.
Audit Your AI Context Layer
Tell us which tools your team uses today. We'll help map the context surface, permissions, stale assumptions, and first reliable agent workflows.
Context Details
Share your workflow and tools
Quick Response Guarantee
We respond to all inquiries within 4 hours.
What Happens Next?
Initial Review
We review your project details and prepare a tailored response.
Strategy Call
30-minute consultation to explore solutions.
Custom Proposal
Detailed project roadmap with timeline, stack, and investment.