Autonomous agents that complete multi-step work, under your control.
Not a chatbot that answers — an agent that acts: plans a task, calls your tools, checks its work, and stops for a human before anything irreversible. Staged autonomy, hard approval gates, in your own cloud — production in 4–8 weeks.
Agents earn their place in workflows that are multi-step, repetitive, and currently eat skilled hours.
Take a request end to end — look up the order, process the return, issue the credit, update the record — instead of a scripted reply. Benefit — lower handle time and contact volume, with resolution instead of deflection.
Triage failing tests, draft fixes, open pull requests, and run the regression suite under the review gates a senior engineer would demand. Benefit — faster cycle time on routine toil, quality held at the gate. McKinsey reports software-engineering and IT functions seeing 10–20% cost reductions from AI (McKinsey, 2025).
Read an invoice, match it to the purchase order, post the clean ones, and route anything ambiguous to a person. Benefit — lower cost-per-transaction and fewer manual-entry errors on high-volume processes.
Investigate an alert, gather diagnostics, attempt a known safe remediation, and escalate with a written summary if it can’t resolve it. Benefit — shorter mean-time-to-resolution and fewer 2 a.m. pages for toil.
Decompose a question, pull from internal and approved external sources, cross-check, and assemble a cited brief. Benefit — analyst hours redirected from gathering to judgment, with traceable sourcing.
Several narrow agents — planner, retriever, writer, checker — coordinated so each does one job well while a supervisor catches the handoffs. Benefit — reliability on complex tasks a single do-everything agent fumbles.
The difference between an agent that earns trust and a prototype that never leaves the sandbox.
We find the workflows where an agent pays off and decide how much autonomy each step gets, run as part of our AI readiness assessment — with the honest “keep this a human task” call included.
We break the goal into discrete, checkable steps the agent can plan over and a reviewer can audit — a sequence you can inspect, not a black box.
The agent acts through permissioned calls into your CRM, ticketing, code, and data systems — each tool scoped to its step, read separated from write, inside the controls your security team already runs.
Irreversible or high-cost actions stop for a human; low-risk ones run unattended — the agent rises up the autonomy ladder only as the evidence earns it. Human-in-the-loop by design, not a bolt-on.
Where one agent overreaches, we split the work across coordinated specialists with a supervising layer that catches a failed handoff before it propagates.
Before production, the agent is tested against a task suite built from your real cases — success rate, tool-call correctness, and the actions that must never fire. Evals are the gate, not an afterthought.
We ship behind shadow mode then a staged rollout, instrument every run for cost, drift, and intervention rate, and train your team to read traces and widen autonomy as confidence grows.
What you get when you hire us — all assigned to you
An agent is only as trustworthy as the delivery discipline underneath it. We don’t yet publish a named agentic case study, so here is the production record we can stand behind.
Aegis AI delivery discipline took a 200+ location chain to twice-a-week releases with zero critical defects, sustained across four years — the same evals-before-launch, staged-rollout, monitor-after process an agent demands. Adjacent example: software delivery, cited for the production discipline.
bjsrestaurants.com ↗
Live and re-engineered continuously since 2012, now used by USC, the LA Rams, and MLB and MLS teams — systems that hold up in production for the long run.
bridgeathletic.com ↗
Marketplace, payments, and transaction infrastructure built end to end; $120M+ processed, acquired by Caterpillar in 2017 — software wired safely into money-moving systems of record.
TechCrunch ↗Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. When an agent shouldn’t be autonomous, we’ll tell you.
Responsible AI is the founding charter. For a system that acts on your behalf, governance is the product, not a checkbox.
Staged autonomy, not all-or-nothing. An agent starts gated and earns each rung as its metrics justify it — the discipline most cancelled projects skipped.
Engine-agnostic. We benchmark OpenAI, Claude, and Gemini on your actual tasks and route to whichever plans and uses tools best.
Founder-led, one accountable lead. No handoffs — the person who scopes the agent answers for what it does in production.
Built to transfer. Prompts, evals, tool layer, and the autonomy charter are assigned to you, your team trained to run and extend the agents.
A chatbot answers; an agent acts. An LLM application or conversational assistant responds to a prompt. An agent takes a goal, breaks it into steps, calls tools against your systems, checks results, and re-plans when one fails. That autonomy is the value and the risk — so task decomposition, tool governance, evals, and approval gates are the whole job.
Three layers. First, scoped tools — each action limited to what the step needs, write separated from read. Second, approval gates — anything irreversible or high-cost stops for a human; the agent starts low on the autonomy ladder, rising as metrics earn it. Third, evaluation and monitoring — we test against your real cases and monitor production for actions that must never fire.
Against a task suite built from your real cases, scored before it touches production: task-success rate, tool-call correctness, intervention rate, and cost-per-task. We set targets at kickoff and report weekly through the pilot — so “it works” is a number you’ve seen, not a vibe from a demo. Most agentic projects that get cancelled skipped exactly this step.
We start with the simplest that works — usually a single well-scoped agent, because every extra agent adds coordination failure modes. We go multi-agent only when one agent is overreaching on a complex task, splitting it into specialists (plan, retrieve, act, validate) with a supervising layer that catches handoff errors before they propagate. The architecture follows the task, not the trend.
Whichever wins your evaluation on planning and tool use for your tasks. We benchmark the candidates on your real cases during design and route accordingly — and because the agent sits behind a model abstraction, switching later is a config change, not a rebuild. See our LLM development services for how we work across all three.
The agent runs in your cloud tenant, under your access controls. Every tool call is scoped and permissioned; writes sit behind approval gates; and every engagement starts with an NDA and a security review. API traffic to major providers isn’t used to train their models by default, and we document every data path and action so your team verifies rather than trusts.
You do — completely. Prompts, evaluation harness, tool layer, the autonomy charter, and the code transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate, audit, and extend it. Keep us on a reduced retainer or take the keys; the engagement is built around the handover.
Most agents reach production in 4–8 weeks under a fixed-scope engagement with one accountable lead, payment tied to the ROI agreed up front. Build cost depends on scope — our AI development cost guide gives real ranges — and run cost is token-and-tool economics we model before building, so the first invoice is a forecast you’ve already seen.
Thirty minutes · No pitch deck
Bring the workflow. We’ll tell you honestly whether an agent fits it, how much autonomy it should have, and what it costs to run.