Operations & RevOps leaders
Pain: Outreach, support and scheduling do not scale; contractors and copy-paste eat the roadmap.
Fit: Agent pipelines for research, drafting and triage with approval queues and CRM logging.
Flagship practice · Agentic AI
AI agents that do real work, governed
We design, build and operate AI agents that follow your rules — discovery, outreach, support, classification and back-office automation — with a human in the loop, observability, and governance baked in from the first prompt.
Yerbabuena Digital
Yerbabuena Digital is an agentic AI studio. We turn repetitive, judgment-light work into agent pipelines — research, drafting, triage, classification, scheduling — wired to your CRM, inbox and data, with approval gates, logs and guardrails designed in. Built on hands-on experience with agent discovery, MCP, and agentic governance for enterprises in finance, healthcare and government.
Who this practice is for
Teams that want automation that ships outcomes — not demos — and need it to stay safe, explainable and compliant.
Data & AI teams
Pain: Agents are appearing across teams with no discovery, no guardrails and no audit trail.
Fit: Agent observability, MCP integrations and policy guardrails tied to your data platforms.
Regulated enterprises
Pain: EU AI Act and GDPR pressure makes ungoverned agents a board-level risk.
Fit: Human-in-the-loop workflows, data minimization and evidence ready for audit (see AI Governance).
From prompt to production — safely
An agent is a software worker that follows a defined goal against your tools and data. The hard part is not the model — it is the boundaries: what the agent may read, write, send and decide, and what must come back to a human. We design those boundaries first, then build.
We work across frameworks — LangChain, CrewAI, Bedrock-class models, Copilot Studio and custom agents — and connect them through Model Context Protocol (MCP) and gateways so your agents reach the right data with the least privilege.
Every pipeline ships with observability: traces, Trustscore-style evaluation, approval logs and guardrails. You can see what each agent did, why, and who approved it — which is what auditors and your CISO will ask for.
Agents also augment our own delivery: we use them to speed up research, content drafts and QA, so premium work lands in days, not months — with senior engineers keeping the wheel.
Return on investment
Where the numbers come from
Illustrative scenarios based on typical engagements — results vary by process maturity, data quality and scope. We scope every pilot together before you commit.
B2B SaaS — outbound pipeline
A scale-up replaced two part-time SDR contractors with an agent pipeline: research, draft, human approve, send — connected to CRM.
- Qualified meetings
- 3× / quarter — Same ICP, better follow-up
- Contractor spend
- −40% — Agent + senior review hours
- Time to first PoC
- 2 weeks — Discover → live agent
Agents scale repetitive outreach when approval gates and logs are designed in from day one.
Financial services — document triage
An operations team spent hours sorting and classifying inbound documents before review. An agent pipeline now pre-classifies, extracts and routes.
- Manual handling time
- −65% — Agent classifies, human reviews exceptions
- Routing accuracy
- ~94% — With human confirmation on low-confidence
- Audit trace
- 100% — Every decision logged with rationale
Agent-augmented triage frees skilled people for exceptions — with a full audit trail.
Capabilities
What we build and automate
Pick one workflow or connect several into a governed agent fabric across your operations.
Agent design & build
Goal-driven workers wired to your tools and data, with clear boundaries.
- LangChain, CrewAI, Bedrock-class, Copilot Studio, custom
- MCP integrations to your systems of record
- Prompts, tools and approval gates scoped with you
- Evaluation harness and golden datasets
Agent discovery & observability
See every agent, what it can reach, and how it performs.
- Inventory of agents, tools and data access
- Traces, Trustscore-style evaluation and metrics
- Drift and failure detection with alerting
- Evidence exports for audit and review
Agent-augmented operations
Real workflows that replace repetitive contractor work.
- Lead research and personalized outreach
- Support triage, summarization and routing
- Document classification and extraction
- Scheduling, reminders and back-office tasks
Guardrails & human-in-the-loop
Safety and control designed in, not bolted on.
- Approval queues for anything requiring judgment
- Data minimization and least-privilege tool access
- LLM guardrails and content policies
- Logs, retention and replay for every action
Use cases
Workloads we know well
Outbound that keeps a human in the loop
Challenge: Founders doing manual outreach; inconsistent CRM hygiene and follow-up.
Outcome: Agent drafts + approval queue; qualified meetings up, contractor spend down (illustrative).
Document triage and routing
Challenge: Skilled reviewers sorting and classifying inbound documents by hand.
Outcome: Agent pre-classifies and extracts; humans handle exceptions with a full audit trail.
Support and scheduling agents
Challenge: Front-line scheduling and support requests overwhelming small teams.
Outcome: Agent triage and scheduling with human confirmation on edge cases and PII guardrails.
Engagement
How an agent engagement runs
From discovery to a governed pilot you can show your auditors — in clear phases.
Discover
We map the process, data, tools and risk. You get a written agent brief: goals, boundaries, approval gates and success metrics — not a vibe.
Architect
We design the agent, tools, MCP integrations and guardrails, plus the evaluation harness. You approve the boundaries before build.
Pilot
We build a live pilot on sanitized data with human-in-the-loop. We measure Trustscore, latency and cost against the baseline.
Operate & govern
We harden, document and hand over — or run it under a governed retainer with monitoring, evaluation and audit evidence.
Before & after
What changes when agents are governed
Before
- Manual, copy-paste processes that do not scale
- Shadow agents with no inventory, guardrails or logs
- Models with broad data access and no approval gates
- No evidence for audit, security or the EU AI Act
After
- Agent pipelines with human approval and CRM logging
- Agent discovery, observability and Trustscore evaluation
- Least-privilege tool access via MCP and gateways
- Traces, retention and audit-ready evidence by design
Outcomes
What clients typically see
Capacity without headcount
Repetitive research, drafting and triage handled by agents — skilled people focus on exceptions and judgment.
Explainable automation
Every agent action traced, logged and replayable — so audit, security and the business can see what happened and why.
Faster time-to-value
Live pilots in weeks, not quarters — senior delivery with agent-augmented research and QA.
Governed by default
Approval gates, least-privilege tool access and guardrails from day one — ready to pair with AI Governance.
Frequently asked questions
Frequently asked questions
Which agent frameworks do you work with?
LangChain, CrewAI, Bedrock-class models, Copilot Studio and custom agents. We are framework-agnostic — we pick the right tool and connect it through MCP and gateways so you are not locked in.
How do you keep agents safe?
Boundaries first: least-privilege tool access, approval gates for judgment calls, LLM guardrails, data minimization and full logging. We pair this with our AI Governance practice for regulated environments.
Can you prove what an agent did?
Yes. We instrument traces, evaluation (Trustscore-style) and decision logs so every action is replayable and exportable for audit.
Do agents replace our team?
No. Agents handle repetitive, judgment-light work; your team approves exceptions and owns outcomes. The point is capacity and consistency, not headcount cuts.
What is MCP and why does it matter?
Model Context Protocol is how agents connect to tools and data safely. We design MCP integrations so agents reach the right data with the least privilege — a foundation for governed agents.
How fast can we pilot?
Typically a live pilot in two to four weeks, depending on data readiness and integrations. We scope boundaries and success metrics in Discover before any build.
Explore our other practices
Yerbabuena Digital
Still navigating between pilot and production?
If you're working through questions around access, architecture, compliance, cost control, or moving a working system into a governed production environment, we can help clarify the next practical step. We respond within one business day with a direct assessment and, where it makes sense, an initial conversation.