Agent Ops
The lifecycle system for keeping agents accurate, trusted, and continuously improving.
Trusted by Our Clients
Why It Matters
Building agents is just the beginning. Without observability, governance, and optimization, you risk drift, downtime, and wasted compute. AgentOps gives you full control—so your AI stays accurate, secure, and cost-efficient in production. It’s the difference between proof of concept and proven performance.
How We Build AI Agents That Actually Work
Ensuring reliability, safety, and continuous improvement for every deployed agent.
Instrument Agents for Observability
We equip agents with robust observability layers—capturing metrics like response times, decision traces, tool usage, and success/failure rates. This visibility helps you monitor agent performance in real-time and spot anomalies early. Logs, dashboards, and alerts provide transparency into agent behavior. This foundation is key for troubleshooting and optimization.
Capture Feedback and Learn
Every interaction becomes an opportunity to learn. We capture user feedback, flag incorrect responses, and identify gaps in understanding. This data feeds into retraining loops, prompt refinement, and agent fine-tuning processes. Feedback mechanisms can be manual or automated, depending on business need. Our goal is to make the agent smarter with every interaction.
Continuously Improve Behavior
Agents aren’t static—they evolve. We apply improvements iteratively using test sets, A/B experimentation, and prompt engineering. Enhancements focus on response accuracy, decision precision, and task success rate. We maintain version histories and rollback capabilities for controlled changes. This enables you to scale confidently and improve with user adoption.
Govern with Safety and Compliance Controls
Safety is embedded at every level. We implement guardrails to prevent hallucinations, misuse of tools, or data leakage. Agents are constrained by role-based access, prompt boundaries, rate limits, and output filtering. For regulated industries, we enforce compliance workflows, audit trails, and explainability features. This protects both business and user trust.
Optimize for Cost and Efficiency
We monitor agent costs across tokens, API usage, compute resources, and integrations. Based on usage patterns, we optimize agent behavior to reduce unnecessary calls, cache repetitive tasks, and balance accuracy with performance. This ensures your AI agents remain sustainable, affordable, and scalable as adoption grows.
Client Success Stories
Discover how we've helped businesses transform with intelligent AI solutions.
Intellectyx, implemented a state-of-the-art GAI chatbot framework, employing machine learning algorithms like tf-idf, word2vec, and cosine-similarity, alongside models such as Llama2, LTSM, and Transformers.
We developed a multi-agent AI system that reimagines how users assess, monitor, and improve data quality, enabling intelligent collaboration, automation, and real-time decision-making.
Multimodal GenAI-powered automated customer service platform for a large Electrical and Electronics Manufacturer, supporting NLP, image, audio, and video inputs for contextual insights and personalized information delivery.
LLM-powered healthcare knowledge assistant enabling scientists to retrieve complex clinical, chemical, and lab-related data using voice and text, reducing research time and improving accuracy in labs.
Narrative Generation Agent integrated with BI tools like Power BI, Tableau, and Qlik, transforming raw dashboard data into real-time natural language insights for faster decision-making.
GEN AI and ML-powered real-time Q&A system that analyzes user queries, recommends high-confidence responses, and continuously learns from user feedback to automate repetitive support functions.
FAQs
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