Agent builds
Tool-calling, RAG, multi-agent
We build AI agents that survive production.
BDArgentumAI designs, builds, and hardens agentic AI for teams that need more than a demo: tool-calling agents wired into your real systems, with evals, guardrails, a cost budget, and a kill switch from day one.
# illustrative example of the controls we ship with every agent $ agent run --task "triage today's inbound requests" --max-steps 12 [plan] classify → extract actions → draft replies → ask human [tool] crm.lookup(account) ✓ read-only [tool] tickets.create(draft) ! needs approval [guard] prompt-injection in msg #17 ✗ refused, flagged [eval] golden set accuracy gate: pass [cost] session spend vs. budget within budget [log] trace → observability ✓ every tool call $ done. step limit respected · human in the loop for writes
AI integration
Into your data, APIs, and workflows
Hardening
Evals, guardrails, telemetry, cost
Training
Live Agentic Production Series
// What we build
From first agent to agent fleet.
Pick the stage you're at. Every engagement ends with something running, measured, and handed back to your team.
01
Custom agent builds
Tool-calling agents and copilots for real workflows: triage, research, document processing, internal assistants, coding agents.
- Step limits and stopping conditions
- Human approval on anything that writes
- Tests and a run log you can read
02
AI integration & RAG
Ground models in your private knowledge and connect agents through MCP, APIs, and event streams.
- Retrieval over your docs and data
- MCP tools, memory between sessions
- OpenAI, Azure OpenAI, Claude, Gemini, Grok, open models
03
Production hardening
Turn the demo into something security and leadership can approve.
- Eval gate in CI
- Layered guardrails, PII redaction
- Telemetry, cost budget, kill switch
04
Multi-agent systems
Planner, parallel implementers, test-writer, and reviewer, with contracts and a gate.
- Role contracts and shared artifacts
- Isolated worktrees where they help
- A gate that checks the work
// How we work
Small, fixed-scope steps. Clear go/no-go.
01
Agent Readiness Assessment
About two weeks. Map the workflow, the data boundary, and the risk.
Out: a ranked shortlist, or a written stop.
02
Pilot Sprint
About four to six weeks. One agent, one workflow, agreed acceptance tests.
Out: a working agent and eval results.
03
Production Build
Milestones. Integrate, harden, and deploy with a rollback path.
Out: a go/no-go you can sign.
04
Run, improve, hand off
Keep the evals current, or train your team and hand the keys back.
Out: a team that can run it.
// Approach
Boring where it matters.
LLMsMCPLangGraph / custom loops
pgvectorQdrantPinecone
OpenTelemetryAWSAzureGCP
- Step limits
- Human approval on writes
- Eval gates
- Cost budgets
- Kill switch
Same controls we teach in the Agentic Production Series.
// Training
Learn the way we build.
About 75% hands-on. Live on Zoom. A recording is included for the paid day. Each seat is $750.
Tue Oct 27, 2026
Agent Zero to Hero: Build Your First AI Agent with Grok Build
Show up curious and leave with a working, tested AI agent you built yourself and can explain: the loop, the tools, the step limit, and the cost.
$750
Register →Thu Oct 29, 2026
Plugged In: Tools, MCP, and Memory for Real-World Agents
Connect agents to your data, APIs, and workflows, give them memory between sessions, and keep a human in the loop for anything that writes.
$750
Register →Tue Nov 3, 2026
Agent Swarms: Multi-Agent Orchestration with Grok Build
Run planner, implementer, test-writer, and reviewer agents in isolated worktrees, with contracts between them and a gate that checks the work.
$750
Register →Thu Nov 5, 2026
Production-Grade Agents: Evals, Guardrails, and Deployment
Turn a demo into an agent the company can trust: evals in CI, guardrails, telemetry, a cost budget, a kill switch, and a go/no-go review.
$750
Register →// Field notes
From the build log.
Agentic AIA season of agentic production: Fall 2026
Since mid-July this blog has published twenty pieces on putting agents into production: workflows before autonomy, evals, MCP, context, approvals, guardrails, cost, latency, retrieval, and injection. Mid-October is a goo
LeadershipWhat done means for an agent project
"We'll know it when we see it" is an expensive phrase in any software project. In agent projects it is worse, because agents fail charismatically. A wrong answer delivered fluently looks a lot like a right one, and a dem
Agentic AIPrompt injection and goal hijack
Any agent that reads email, tickets, documents, or web pages will eventually read a sentence written to manipulate it. Sometimes it is crude: "ignore your previous instructions." Sometimes it is subtle: a support ticket
Have a workflow that should be an agent?
Tell us the workflow. You get a reply within 24 hours, and a written proposal or an honest not-a-fit.
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