Service · AI
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.
Plan · act · check · gate — then re-plan
The real problem
Why most agentic AI projects stall before production.
A demo agent and a production agent are different animals: turned loose on real workflows, a prototype takes a wrong step and quietly corrupts a record before anyone notices.
The gap is never the model's reasoning — it's the engineering that makes an autonomous system safe to trust: checkable steps, constrained tool calls, evals before production, and a human exactly where a mistake is expensive. That surrounding system is agentic AI development.
Of organizations are scaling an agent in the enterprise, against 62% merely experimenting.
Where it deploys
Where enterprises deploy AI agents — and what each one does.
Agents earn their place in workflows that are multi-step, repetitive, and currently eat skilled hours.
Customer-operations agents
Take a request end to end — look up the order, process the return, issue the credit, update the record — instead of a scripted reply.
Lower handle time and contact volume — resolution, not deflection.
Software-engineering agents
Triage failing tests, draft fixes, open pull requests, and run the regression suite under the review gates a senior engineer would demand.
Faster cycle time on routine toil, quality held at the gate (10–20% IT cost cuts, McKinsey).
Back-office & finance agents
Read an invoice, match it to the purchase order, post the clean ones, and route anything ambiguous to a person.
Lower cost-per-transaction, fewer manual-entry errors at volume.
IT operations & remediation agents
Investigate an alert, gather diagnostics, attempt a known safe remediation, and escalate with a written summary if it can't resolve it.
Shorter mean-time-to-resolution, fewer 2 a.m. pages for toil.
Research & analysis agents
Decompose a question, pull from internal and approved external sources, cross-check, and assemble a cited brief.
Analyst hours redirected from gathering to judgment, with traceable sourcing.
Multi-agent workflows
Several narrow agents — planner, retriever, writer, checker — coordinated so each does one job well while a supervisor catches the handoffs.
Reliability on complex tasks a single do-everything agent fumbles.
40%+ of agentic projects will be canceled. Almost always for skipping the engineering that makes autonomy safe — evals, scoped tools, approval gates. The agent starts gated and earns each rung as its metrics justify it.
As of June 2026 · revisit quarterly
What agentic AI is doing to enterprise work — the measured impact.
Independent industry findings, cited as third-party evidence — not Silicon Prime's own client results.
Of enterprise apps will include agentic AI by 2028 — up from under 1% in 2024.
Of day-to-day work decisions made autonomously by 2028 — up from 0% in 2024.
Software-engineering and IT cost reductions from AI in the functions adopting fastest.
What's included
What agentic AI development covers.
The difference between an agent that earns trust and a prototype that never leaves the sandbox.
Use-case scoping & autonomy mapping
We find the workflows where an agent pays off and decide how much autonomy each step gets — with the honest "keep this a human task" call included.
Task decomposition & planning
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.
Governed tool use & integration
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 your controls.
Approval gates & staged autonomy
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.
Multi-agent orchestration
Where one agent overreaches, we split the work across coordinated specialists with a supervising layer that catches a failed handoff before it propagates.
Agent evaluation & guardrails
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.
Deployment, monitoring & enablement
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
How it runs
How an agentic AI engagement runs.
The delivery model behind all our AI development work, tuned for autonomous agents.
STEP 01
Discover
Scope the workflow, map where the agent acts versus where a human signs off, and agree the success metrics.
Output: a ranked plan & an autonomy policy
STEP 02
Design
Build the evaluation suite from your real cases and choose the model on your tasks, not on hype.
Output: an agent task suite & a tool/integration architecture
STEP 03
Build
Develop the agent in your own cloud tenant, wired to your systems through governed tools, with approval gates and guardrails in place.
Output: a working agent behind your access controls
STEP 04
Deploy & enable
Shadow mode, then a supervised pilot, then widening autonomy as the evals hold — success and intervention rates measured weekly, your team trained to operate it.
Output: a production agent & a team that owns it
Track record
Before you let software act on its own.
Autonomy is earned by the delivery discipline underneath it — evaluate before launch, widen scope in stages, watch what it does in production. We don't yet publish a named agentic case study, so we point to the one engagement that proves we run exactly that discipline at scale.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded 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.
Aegis AI delivery discipline · 200+ locations · 4 yrs
BJ's Restaurants — our process moved a 200+ location chain from every-two-weeks to twice-a-week releases, with zero critical defects sustained across four years. That is the evals-before-launch, staged-rollout, monitor-after loop an autonomous agent lives or dies by. bjsrestaurants.com ↗
Adjacent evidence — a software-delivery engagement, cited for the production discipline an agent demands, not an agent deployment.
Why build your agents with us.
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 canceled projects skipped.
Eval-driven, not demo-driven. Success is a task suite scored before launch and monitored after — the step most canceled agentic projects skipped.
Engine-agnostic, built to transfer. We benchmark OpenAI, Claude, and Gemini on your tasks; prompts, evals, tool layer, and code are assigned to you under full IP.
Where it earns its keep first
Where agents earn their keep first.
Fintech & back office
Invoice-to-record, reconciliation, and operations agents where every write sits behind an approval gate and an audit trail.
Fintech software →Ecommerce & customer ops
Customer-operations agents that resolve returns, credits, and account changes end to end against your order systems.
Ecommerce software →SaaS & engineering teams
Software-engineering and IT-ops agents that triage, draft, and remediate under the review gates a senior engineer would demand.
SaaS software →Questions buyers ask before they build.
Thirty minutes · no pitch deck
Ready for an agent that acts — and that you can trust?
Bring the multi-step workflow eating skilled hours — we'll tell you honestly which steps an agent should own, where the human gate goes, and what it takes to get to production.