Technologies
AI agents that do the work — not just describe it
Most 'AI agent' demos are a single LLM call wearing a costume. Real agents need planning, tools, state, evals, guardrails, and observability. We build the production-shaped version: MCP-native, audit-trailed, human-in-the-loop where it matters.
What We Build
Production-shaped agentic systems — not demo notebooks
Single-Step Agents
Goal-driven assistants that use a fixed toolbelt to answer a question or complete a task. The right choice for 60% of 'agent' use cases — simpler, faster, cheaper than multi-step.
Multi-Step Workflows
Plan, execute, observe, replan loops built on LangGraph, CrewAI, or custom orchestration. State machines with checkpoints, retries, and circuit breakers.
MCP-Native Tool Use
Model Context Protocol servers that expose your tools to any MCP-compatible model. Standardized, auditable, model-agnostic.
Guardrails & Approval Gates
Output validation, PII redaction, prompt-injection defense, and human-in-the-loop checkpoints for irreversible actions (sending money, deleting data).
Long-Running State
Durable execution with Workflow DevKit, Temporal, or Inngest. Crash-safe, pausable, restartable — the shape real agents need.
Agent Evals
Trajectory-level evals (did the agent take the right path?), outcome evals (did it produce the right result?), cost/latency budgets. Without evals, agents drift silently.
The Stack
Model-agnostic, protocol-first, observability-from-day-one
Models
- Claude (Anthropic)
- GPT (OpenAI)
- Gemini (Google)
- Llama / Mistral (self-hosted)
- Open-source via Bedrock, Together, Groq
Orchestration
- LangGraph
- CrewAI
- Workflow DevKit (Vercel)
- Temporal
- Inngest
- Custom state machines
Tool Protocols
- MCP (Model Context Protocol)
- OpenAPI tool calling
- Function calling
- Vercel AI Gateway
Retrieval
- pgvector
- Pinecone
- Weaviate
- Qdrant
- Hybrid search (BM25 + vector)
- Cohere / Voyage reranking
Evals & Observability
- Braintrust
- LangSmith
- OpenTelemetry + Honeycomb / Datadog
- Custom rubric harnesses
Guardrails
- NeMo Guardrails
- PII redaction (Presidio)
- Prompt-injection classifiers
- Output schema validation (Zod, Pydantic)
How We Build Them
Eval-first, simplest-architecture-that-works, observability before scale
Define the agent's job
Concrete success criteria, refusal conditions, tool boundaries, and the maximum cost/latency per task. Without these, an agent is just an LLM with anxiety.
Pick the simplest architecture
Most 'agent' problems are actually a single LLM call with one tool. Resist the urge to build a tree of agents until the simpler thing fails.
Wire the tools
MCP server for tool exposure. OpenAPI specs for the agent to discover. Idempotent, auditable, sandboxed where the blast radius matters.
Build the eval harness
Trajectory eval (Braintrust, LangSmith, custom). Score every PR against frozen test cases. Block merges that regress key metrics.
Ship with observability
Trace every tool call, prompt, retry, and decision. OpenTelemetry to your observability stack. You'll need it on day three when something goes weird.
Iterate on the loop
Production tells you which prompts are too vague, which tools the agent avoids, which states it gets stuck in. Tighten weekly until the eval scores plateau.
Define the agent's job
Concrete success criteria, refusal conditions, tool boundaries, and the maximum cost/latency per task. Without these, an agent is just an LLM with anxiety.
Pick the simplest architecture
Most 'agent' problems are actually a single LLM call with one tool. Resist the urge to build a tree of agents until the simpler thing fails.
Wire the tools
MCP server for tool exposure. OpenAPI specs for the agent to discover. Idempotent, auditable, sandboxed where the blast radius matters.
Build the eval harness
Trajectory eval (Braintrust, LangSmith, custom). Score every PR against frozen test cases. Block merges that regress key metrics.
Ship with observability
Trace every tool call, prompt, retry, and decision. OpenTelemetry to your observability stack. You'll need it on day three when something goes weird.
Iterate on the loop
Production tells you which prompts are too vague, which tools the agent avoids, which states it gets stuck in. Tighten weekly until the eval scores plateau.
Where We Start
Agent-shaped engagements teams ask us for most
Customer Support Copilot
Agent that triages tickets, drafts replies, pulls account context, and escalates with full reasoning trace. Cuts average handle time 40–60%.
Sales Research & Outreach
Researches prospects, drafts personalized emails, files notes in CRM. Reviewed by humans before send, fully auditable after.
Internal Operations Agent
Runs onboarding/offboarding workflows, provisions access, files HR paperwork. Human approval for irreversible steps.
Code & Doc Search Agent
Searches your codebase, wiki, and tickets with reranking and citation. Replaces the 'who knows about X?' Slack thread.
Document Processing Agent
Extracts structured data from PDFs, invoices, contracts. Confidence scoring, human review queue for low-confidence outputs.
DevOps Incident Co-Pilot
Pages alongside on-call. Pulls relevant runbooks, queries observability stack, drafts incident timelines, suggests next steps. Never executes destructive ops without human sign-off.
Common Questions
Do I actually need agents, or just a chatbot?
How do you stop an agent from doing something destructive?
What's MCP and why does it matter?
How do you handle the cost?
Can the agent learn from feedback?
Domains we've shipped in
Stuck between 'cool demo' and 'production agent'?
We design the loop, build the tools, write the evals, and ship the thing your customers actually use.
Related Solutions
AI & Machine Learning
LLM integration, RAG systems, evals, fine-tuning, and production ML.
Data Engineering
Snowflake, Databricks, BigQuery, dbt, Airflow — modern data stack from ingest to activation.
Enterprise Blockchain
Supply-chain provenance, tokenized assets, settlement rails — audited and production-grade.