Everyone's talking about AI agents. Your board wants an 'agentic AI strategy.' But most agentic AI projects fail in production—hallucinations, security gaps, runaway costs. I help you cut through the hype and build production agents that actually work.
Your board wants an 'agentic AI strategy' but nobody knows what production-ready actually looks like.
AI agent demos are impressive. Production deployments are risky without proper guardrails and oversight.
Tool-calling agents can execute harmful actions—how do you build safe autonomous systems?
Evaluation is genuinely hard. How do you measure if an AI agent is doing the right thing?
A systematic approach to building AI agents that are safe, effective, and production-ready. From use case identification to deployment with human oversight.
Identify high-value use cases where AI agents add real value. Not everything needs to be autonomous.
Architecture for safety: guardrails, human-in-the-loop workflows, rollback mechanisms, and clear boundaries.
Implementation with proper evaluation frameworks—not vibes-based testing. Real metrics for agent performance.
Production deployment with monitoring, observability, and governance. Agents you can actually trust.
A structured approach to building AI agents that are safe, effective, and production-ready. Developed from real-world deployments where agents handle critical business processes with appropriate human oversight.
You want to move beyond chatbots to truly autonomous AI systems. You understand the risks and want proper guardrails. You're looking for production agents, not impressive demos.
Chatbots respond to queries. Agents take actions—they can call APIs, execute code, make decisions, and complete multi-step workflows autonomously. This power comes with risks: agents can make mistakes at scale, which is why proper guardrails are essential.
Yes, with the right architecture. The key is identifying appropriate use cases, building robust guardrails, implementing human-in-the-loop oversight for high-stakes decisions, and having rollback mechanisms. Not every process should be automated by agents—we help you identify where they add value safely.
Through layered defenses: input validation, output verification, rate limiting, anomaly detection, human approval gates for high-impact actions, and comprehensive logging. We design agents that fail safely and can be rolled back when needed.
The framework depends on your use case and existing tech stack. I work with LangChain, LlamaIndex, Anthropic's Claude tools, OpenAI Assistants, and custom implementations. The methodology matters more than the framework—we choose based on your specific requirements.
Costs depend on complexity: a focused single-purpose agent (e.g., customer service triage, document processing) takes 4-8 weeks to build and deploy. Multi-agent orchestration systems with complex workflows take 8-16 weeks. Ongoing infrastructure costs depend on volume and hosting model. Contact us for a detailed quote based on your specific requirements.
Proven high-ROI agent use cases include: (1) Workflow automation—expense approval, document routing, meeting scheduling with clear rules and limited scope, (2) Development assistants—writing tests, fixing bugs, generating documentation with human review before merge, (3) Research agents—gathering and synthesizing information for human decision-making, (4) Customer service triage—categorizing requests, collecting information, and preparing responses for human review. Start with well-defined processes that have clear success criteria and human fallback.
Yes, with proper architecture. Key requirements: data minimization (agents should only access data they need), audit trails (comprehensive logging of all agent decisions and actions), human oversight (approval gates for high-stakes actions), transparency (users must know they're interacting with AI), and right to explanation (ability to explain why the agent took a specific action). We build compliance into agent architecture from day one, not as an afterthought.
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