What We Build With It
We engineer production-grade AI agents for high-value, well-defined tasks, moving beyond simple automation.
Autonomous Research Agents
To monitor sources, gather intelligence, and compile summarized reports on specific topics, 24/7.
Intelligent Data Processing Agents
To ingest data from unstructured sources (like PDFs or emails), clean it, and load it into structured databases.
Customer Support Triaging Agents
To analyze incoming support tickets, categorize them, and route them to the correct team with a concise summary.
Why Our Approach Works
We apply rigorous engineering to AI agents, ensuring they are reliable, observable, and deliver measurable business value.
Clear Boundaries, Reliable Execution
An agent that tries to do everything will fail. We define narrow, high-value tasks, building agents that execute with extreme reliability within defined operational envelopes.
Deterministic Design in an LLM World
While LLMs are non-deterministic, we engineer the agent's surrounding framework to be predictable, using structured inputs, validation, and state machines to control flow.
Humans Always in the Loop
For critical tasks, we design agents with clear escalation paths and human approval for high-stakes actions, ensuring transparency and trust.
Our Go-To Stack for AI Agents
We build robust, observable agents using a combination of LLMs, orchestration frameworks, and traditional software engineering tools.
Agent Frameworks
LangChain, LlamaIndex for orchestration and prompt management.
Core LLMs
OpenAI (GPT-4), Anthropic (Claude 3), and fine-tuned open-source models (Llama, Mistral).
Orchestration & Scheduling
Prefect, Dagster for workflow management; Kubernetes for hosting.
Tooling & Connectivity
Custom Python tools, API clients (REST, GraphQL), database connectors for agent actions.
Monitoring & Observability
Prometheus, Grafana, custom logging for tracing agent decisions and actions.
Memory & State Management
Vector databases (Pinecone, Weaviate) and key-value stores (Redis) for managing conversational history and state.
Frequently Asked Questions
How is an AI agent different from a simple RPA bot?
+RPA bots follow fixed rules on structured interfaces. AI agents handle ambiguity, understand unstructured data, and make complex, context-aware decisions. They augment, rather than just automate.
Can an agent really make decisions without supervision?
+For low-stakes, reversible tasks, yes. For anything critical, we advocate a ‘human-in-the-loop’ design where the agent proposes, and a human approves, ensuring controlled autonomy.
How do you handle errors or unexpected situations?
+We build robust error handling, retry logic, and state management. If an agent gets ‘stuck’ or confused, it halts and escalates to a human operator with a full report, preventing silent failures.
Do you build multi-agent systems?
+Yes. For complex workflows, we design ‘agentic swarms’ where specialized agents collaborate—for example, one agent researches, another drafts, and a third audits the output—leading to much higher quality results.
How do you keep AI agents secure?
+Security is built-in. We implement ‘prompt injection’ protection, strictly limit agent permissions using ’least privilege’ APIs, and provide immutable audit logs of every action an agent takes.
Are AI agents expensive to run?
+They can be if not optimized. We use ‘agentic design patterns’ that minimize unnecessary LLM calls, utilize smaller fine-tuned models for specific tasks, and implement caching to ensure the system is both performant and cost-effective.