Building an AI agent that gets smarter with every interaction is possible with the right platform. Not through manual updates or periodic retraining, but through a fully automated feedback loop that monitors conversations, identifies issues, creates test cases, and continuously improves agent performance. Here's how to build it using Cekura's testing and observability platform.
The Problem: Static Agents, Dynamic Products
Like many fast-growing startups, the classic problem is that products evolve faster than AI agents can keep up. Production conversations reveal issues—hallucinations, knowledge gaps, incorrect responses—but by the time these are manually identified and fixed, new problems emerge. What's needed is a system that can:
- Monitor all conversations to detect issues in real-time
- Create test cases from problematic conversations
- Run automated metrics to identify performance issues
- Improve agents iteratively based on findings
- Keep knowledge bases current with the latest documentation
The result? A fully autonomous AI agent that learns from every interaction.
Architecture Overview: The Self-Improvement Cycle
Build a self-improving agent using Cekura's monitoring and testing infrastructure to create a continuous improvement loop:
1. Your AI Agent
At the core is your AI agent integrated through webhooks. Your agent handles conversations in real-time while you use Cekura to monitor every interaction.
What You Can Build:
- Stateful conversations: Agents that maintain context across multi-turn dialogues
- Knowledge base injection: Dynamically load your latest knowledge base as context
- Real-time monitoring: Capture every conversation for analysis
- Continuous optimization: Improve agents based on metrics and test results
2. Knowledge Base Connectors: Always-Fresh Context
Your agent's intelligence is only as good as its knowledge base. Use Cekura's Knowledge Base Connector system to automatically keep your agent's knowledge base up-to-date.
How It Works:
- Configure connectors through the Cekura dashboard to scrape your knowledge sources
- Set sync intervals based on your update frequency (daily, hourly, etc.)
- Built-in security features (URL validation, SSRF protection)
- Automatic content extraction and cleaning
Result: Your AI agent always has access to the latest context without manual updates.
3. Observability & Metrics: Finding Issues Automatically
Here's where the magic happens. Use Cekura's monitoring system to capture every production conversation and run automated metrics to identify issues in real-time.
The Self-Improving Loop: Where the Magic Happens
Here's how everything connects to create a truly autonomous system:
Step 1: Conversation Monitoring → Test Case Creation
Monitor every conversation through Cekura's observability system. When metrics fail, save those conversations as reusable test cases that can be replayed and analyzed for agent evaluation.
Step 2: Automated Performance Analysis with Metrics
Set up automated metrics in Cekura to run on every conversation and identify issues. These metrics evaluate critical aspects like:
- Hallucination Detection: Did the agent make up information?
- Response Quality: Were answers accurate and helpful?
- Knowledge Gaps: Did the agent fail to answer questions it should know?
- Sentiment Analysis: Was the customer satisfied?
- Resolution Completeness: Was the issue fully resolved?
When metrics fail, alerts are automatically sent to your team, signaling specific issues that need attention.
Step 3: Test-Driven Agent Improvement
When issues are identified through metrics, use Cekura's testing framework to implement a test-driven improvement cycle:
- Conversation becomes test case: The problematic conversation is saved as a test case in Cekura
- Reproduce the issue: Run the test case to confirm the problem
- Fix the agent: Update prompts, add documentation, or modify agent logic
- Validate the fix: Re-run the test case to verify the issue is resolved
- Prevent regression: Test case remains in your suite to catch future regressions
Example Workflow:
Conversation: "How do I integrate LiveKit with custom websockets?"
↓
Metric fails: Knowledge Gap Detected
↓
Test case created from conversation
↓
Knowledge base updated: "LiveKit Custom WebSocket Integration Guide"
↓
Knowledge Base Connector syncs new docs automatically
↓
Test case re-run: Agent now answers correctly
↓
Test case added to regression suite
Step 4: Continuous Monitoring → Always Improving
Close the loop by:
- Scheduling automatic syncs: Configure KB connectors to fetch fresh context on your schedule
- Running metrics on every conversation: Issues are detected immediately
- Accumulating test cases: Every fixed issue adds to your regression test suite
- Improving agent performance: Each cycle makes your agent smarter and more reliable
Technical Deep-Dive: Building with Cekura
Knowledge Base Connectors
Use Cekura's Knowledge Base Connector system to enable automatic knowledge syncing. Through the Cekura UI, you can configure website scrapers that run on scheduled intervals (daily, hourly, etc.) to keep your AI agent's knowledge base continuously updated.
How It Works:
- Configure connectors through the Cekura dashboard
- Set sync intervals based on your knowledge base update frequency
- Built-in security features (URL validation, SSRF protection)
- Automatic content extraction and cleaning
Result: Your AI agent always has access to the latest context without manual updates.
Conversation Monitoring & Test Case Management
Use Cekura's monitoring system to capture production conversations and transform them into test cases for continuous evaluation.
Key Capabilities:
- Automatic Test Case Creation: Production conversations can become reusable test cases
- Test Case Replay: Re-run conversations to validate agent improvements
- Regression Testing: Build a test suite that prevents old issues from returning
- Performance Tracking: Monitor agent improvement over time
Built-in Metrics Available:
Conversation Quality:
- Hallucination Detection: Identifies when agents make up information
- Sentiment Analysis: Evaluates customer satisfaction from conversation tone
- Interruption Detection: Measures when agents talk over customers
- Response Latency: Tracks time between customer questions and agent responses
Technical Performance:
- Infrastructure Issues: Detects audio quality, connection problems, timeouts
- Speech-to-Text Accuracy: Evaluates transcription quality
- Pronunciation Analysis: Assesses agent pronunciation clarity
- Silence Detection: Identifies awkward pauses or dead air
Agent Effectiveness:
- Customer Satisfaction (CSAT): Automated CSAT evaluation from conversation
- Spelling Analysis: Checks for spelling errors in chat conversations
- Voice Consistency: Ensures agent maintains appropriate tone throughout
Configure Cekura to automatically trigger alerts to your team when any metric fails its threshold, enabling rapid identification of issues. Each failed conversation can be saved as a test case, creating a growing regression test suite. You can also create custom metrics tailored to your specific use cases and workflows.
Conclusion: Building Autonomous AI Agents with Cekura
Building a truly self-improving AI agent isn't about AI magic—it's about applying test-driven development to AI agents using the right platform. Here's the workflow:
- Monitor every conversation using Cekura's observability system
- Run automated metrics to detect issues in real-time
- Create test cases from failures by converting problematic conversations into tests
- Fix and validate by improving your agent and re-running tests
- Build regression suites to prevent old issues from returning
- Keep knowledge fresh with automatic knowledge base syncing
The result? An AI agent that learns from every mistake, continuously improving through a feedback loop of monitoring, testing, and refinement. Use Cekura to build AI agents that get smarter every day, scaling quality at the speed of your product development.