AI Agent Evolution System
Stop Shipping AI Blind
CoAgent gives AI engineers end-to-end testing and monitoring with domain-specific evals, from user intent through AI reasoning to business outcomes.
Cut debugging time by 90%. Catch failures before users. Protect Customer Trust.
You See
Latency and Error Rates
Token usage and Cost
Model performance scores
You Don't See
Why agents misunderstand
user intent?
Where did the reasoning
break down
Did it pass the
domain-specific validations?
What action did the user
actually take?
Did it drive the business
outcome you care about?
Fondations for Production Gen AI Agents and Apps
Gain end-to-end visibility and control from development through production, so you can operate, and scale AI with confidence.
Trace
Track from user intent through AI reasoning to business outcomes. See exactly where things break in your chain.
Test
Test dynamically on live conversations. Replay production failures with different configs. Build domain-specific evals that match your business logic.
Validate
Run domain-specific quality checks. Validate tool calls, context quality, and outputs against your business rules. Catch issues before users do.
Ship
Deploy with confidence knowing you can trace any issue and validate quality at every step. Release faster without breaking customer trust.
Monitor
Real-time tracking of token usage, costs, latency, and quality metrics. Alert on domain degradation, not just generic errors. Operational visibility for AI systems.
Improve
Capture expert corrections and human feedback. Turn production insights into better test cases and golden datasets. Iterate based on real usage.
Sandbox
Evaluate multiple context configurations with any LLM
CoAgent Sandbox helps you to test the quality and performance of your context, prompts, tools with any Generative AI model. Small or large. Local or APIs. Hyperscalers or foundation model providers. You choose your config and setup in minutes.
Define assertions that matter for your business. Semantic validation, output checks, domain-specific rules. When the AI produces something, validate it against what should happen in your world.
Monitoring
Zoom in and out on traces to identify quality baselines
Search across rich logs and traces. Annotate failures with team insights. See granular performance of prompts, tool call sequences, and context utilization.
Compare and annotate on traces to develop deep quality assertion patterns to iteratively improve agent performance.
Test Studio
Build deep domain specific evaluations and validations
Define what "working" means for your use case. Semantic assertions, output validation, cost boundaries, domain specific deep data validations and evals. Know immediately when reality diverges from expectations.
Go beyond synthetic data and static golden datasets. Incorporate user feedback, human in the loop annotations, and iterative topic modelling for effective pre-processing, post-processing, fine-tuning, and model distillation.
Blog
Building AI Systems That Work
Practical learnings on evaluation, testing, and operations from engineers shipping agents to production.
