Build, Scale, Govern, Optimize. The four-pillar agent practice on Google's Gemini Enterprise Agent Platform, ADK delivery, A2A v1.2 interop, Model Armor at the gateway.
Reference architectures and ADK implementations for the agents your roadmap already calls for. We write the code; your team owns it. No retained rights, no platform lock-in beyond Google Cloud itself.
The Agent Development Kit is the SDK we ship in. ADK Python v1.32 for production health-plan, financial-services, and platform-engineering work; ADK TypeScript v0.9 for front-of-house product agents that need to live next to your existing Node services. Both targets get the same evaluation harness, the same observability wiring, and the same handover repo at the end of the engagement.
Every engagement begins with a one-page architecture diagram named to your domain. Code reviews against your house style. Pull requests, not zips.
Production agents rarely run alone. We design orchestrator-and-worker topologies on the A2A v1.2 protocol. The Agent-to-Agent standard now under the Linux Foundation, where Zinch sits on the working group. Named handoff contracts, typed messages, retry semantics your SRE will recognize.
Interop with non-Google agents via A2A is built in. We've done it. We'll show you the trace.
Stateless agents are demos. Production agents remember. Across sessions, across users, across tools. Memory Bank is the managed store on Gemini Enterprise Agent Platform for persistent agent context, and we wire it correctly: scoped to your tenant, encrypted with your CMEK keys, queryable from the eval harness so regressions catch before deploy.
An agent that works on a developer laptop is not an agent. We deploy on Agent Runtime with the load tests, failure-mode runbooks, and on-call posture your platform team will sign off on the first time.
Agent Runtime is the managed runtime on Gemini Enterprise Agent Platform. Autoscaled, regionally pinned, with first-class A2A and tool invocation. We ship three deployment patterns: single-tenant for regulated workloads, pooled for internal-tooling fleets, and hybrid for the case where one agent crosses the boundary. Terraform modules included.
Golden-set regressions, agent-trace inspection, and policy-gate assertions wired to your CI on day one. Not an afterthought. Not a separate workstream. The harness blocks merges that regress agreed metrics, same shape your code review process already enforces for code, applied to agent behavior.
Policy gates, audit trails, and BAA-ready data handling, built before procurement asks. The pillar your CIO will read first.
Every agent we ship is registered the moment it's deployed: name, owner, policy class, data-handling tier, eval set. The registry is the source of truth your security team queries before a new tool gets added, and the artifact your auditor walks through line by line. Not a wiki page. A typed inventory.
Model Armor is Google Cloud's policy-enforcement layer for prompts, responses, and tool calls. We deploy it as the gateway in front of every production agent. Not a sidecar, not a wrapper. The actual ingress. Prompt-injection screening, jailbreak detection, sensitive-data redaction, and per-agent allow-lists, all configured against your data classification policy. The gateway logs every decision; the registry references those logs by agent name.
HIPAA on healthcare engagements, SOC 2 mappings on financial-services engagements, FedRAMP Moderate where the workload demands it. Under Google's BAA, with our subprocessor list and our incident-response runbook. The deliverable is not a slide deck; it's the binder your security review team will accept.
Most production agent bills are 60% larger than they need to be on month one. We measure cost-per-decision against the metric your CFO already tracks, route requests across the Gemini model family by task complexity, and tune latency budgets per tool. The savings pay for the engagement before the engagement ends.
The legacy Vertex AI SDK reaches end-of-support on June 24, 2026. Code targeting it will continue to compile after that date and continue to break in production after that. We migrate ADK Python and ADK TypeScript codebases to the Google Gen AI SDK on a fixed-fee schedule, with a parity test suite that proves the cutover is silent to your callers.
Two-week assessment. Four-to-eight-week migration depending on surface area. Diff reports, regression coverage, and a versioned cutover plan you can hand to release management.
Read the migration guide →Fixed scope, fixed price, named SE on every engagement. Your team owns the code at the end of all three.
One agent, one workflow, on your data, in your project. The fastest way to a working artifact.
A pilot promoted to production: orchestrator, two workers, governance posture, observability wired to your stack.
A full multi-agent system with cross-team handoffs and the documentation a CIO will hand to the board.
One agent, one workflow, your code at the end of it. Named SE on the call within two business days.
