Back home
[ 01 // AI Agents ]Overview
Autonomous agents engineered for production.
We design multi-step agent systems that plan, call tools, retrieve knowledge, and recover from failure — deployed with the observability, guardrails, and evaluation harness required to run in front of real users.
What we build
- Task agents — research, summarization, extraction, and workflow automation with tool use and memory.
- Multi-agent orchestration — supervisor / worker graphs with LangGraph, AutoGen, and CrewAI, including deterministic hand-offs and human-in-the-loop checkpoints.
- Retrieval-augmented agents — hybrid search over Pinecone, Weaviate, pgvector, or Qdrant with re-ranking and citation guarantees.
- Enterprise integrations — Slack, Salesforce, Jira, Notion, Zendesk, and internal APIs via typed tool schemas.
How we engineer them
- Deterministic graphs where correctness matters; free-form planning where it doesn't.
- Structured outputs with schema validation on every tool call.
- Offline evaluation sets, regression tests, and trace-level tracking with LangSmith or Langfuse.
- Cost, latency, and token budgets enforced at the runtime layer.
- Prompt-injection defenses and PII scrubbing built in.
Stack
LangGraph, LangChain, AutoGen, CrewAI, AWS Bedrock, OpenAI, Anthropic, Pinecone, Weaviate, pgvector, Redis, Postgres, Temporal.
Typical engagement
Two-week discovery and prototype, followed by a 6–10 week production build with evaluation, monitoring, and hand-off. Pilots ship to internal users within the first month.