Sphere wins 2026 Global Recognition Award
Sphere Partners
Agentic AI
Production agentsApproval gatesAudit trailsScoped permissionsPortable architecture

Build agents that work inside real operations.

Sphere builds production agents that read your data, take action across your systems, and route decisions to your team when approval is required.

Find your agentExplore use cases
Production agent workflow
Monitored

Workflow run

Read requestInput
Check policy and source dataGround
Prepare system actionApprove
Update records and notify teamLogged

Connected systems

CRMCases, customers, opportunities
ERPInvoices, POs, ledger data
Data platformPolicies, knowledge, analytics
Internal toolsTickets, messages, approvals
4-7dTeam can start
6wkDelivery plan
100%Action logging target
HumanApproval where needed
Built inside your systemsAgents connect to the tools and data your teams already use
Team starts in 4-7 daysCross-functional AI engineering pod moves quickly
Approvals and audit includedHuman gates, decision logs, and traceability by design
Portable architectureMultiple models, APIs, and platforms supported
Where agent projects get stuck

Moving from AI answers to AI actions changes the risk profile.

The moment an AI system can update records, trigger workflows, or coordinate decisions, it needs permissions, monitoring, approval rules, and a delivery team that understands production operations.

01

Pilots do not survive production

Demos work in isolation, but break when connected to real data, real users, and real system constraints.

See the production shift
02

Agents need safe system access

CRM, ERP, data platforms, and internal tools need scoped permissions, logged calls, and approval gates.

Review guardrails
03

Teams need visibility

Operations leaders need workflow traces, decision logs, audit trails, and clear ownership for every action.

View operations model
04

Reliability must be tested

Complex workflows need evaluation harnesses, regression checks, fallback paths, and ongoing monitoring.

See delivery process
05

Vendor lock-in is avoidable

Portable agent architecture helps teams change providers without rebuilding the entire workflow.

Read FAQ
The agentic shift

From AI assistant to operational agent.

Companies are moving from AI that answers questions to AI that runs parts of operations. Sphere focuses on the production layer where most systems break.

From

Pilot that never shipsDemo
No safe system accessBlocked
No action traceOpaque
Complex workflows failBrittle
Single-vendor dependencyLocked

To

Working workflow in your environmentLive
Scoped CRM, ERP, and tool accessSafe
Full decision and action logTraceable
Regression checks and fallbacksStable
Portable model and API designFlexible

A production agent is not just a chatbot with tools. It is a governed workflow system with permissions, approval gates, monitoring, logs, and a delivery team responsible for making it work in the real operating environment.

What the agent runsWorkflow steps, tool access, permissions, approval points
How decisions stay controlledHuman gates, escalation rules, full decision log
Where it runsAPIs, queues, identity, logs, monitoring, connectors
How performance stays stableRegression checks, scorecards, fallback paths
Production
Agent
Production agent model

A production agent, running inside your operation.

Sphere builds the orchestration, grounding, permissions, monitoring, and control points needed for agents to operate inside actual business workflows.

Workflow orchestration

Define the steps, tools, source data, and business rules the agent must follow.

Human control points

Place approval gates where customer, financial, regulatory, or operational risk requires review.

Operations dashboard

Show workflow traces, decision logs, system calls, evaluation scores, and fallback events.

How Sphere builds

A 6-week path from workflow framing to production validation.

The page should make the delivery motion obvious: define the workflow, build the first agent, prove reliability, then expand autonomy only where controls are stable.

Phase 1

Workflow + risk framing

Map the workflow, define tool access, decide human approval points, and set success metrics.

Week 0-1
Phase 2

Build the agent workflow

Implement orchestration, grounding where needed, and the first deployable increment.

Weeks 2-4
Phase 3

Prove reliability

Add evaluation harnesses, regression checks, monitoring, and fallback paths before expansion.

Weeks 4-6
Phase 4

Scale responsibly

Add more tasks, integrate more systems, and expand autonomy only where controls prove stable.

Weeks 6+
Enterprise-safe agents

Guardrails are not optional when agents can take action.

When an agent can update records, send messages, or trigger transactions, the system needs control, observability, and accountability by design.

Human approval where it matters

Agents prepare actions. Your team approves or escalates high-impact steps.

Scoped system access

Agents only see and use the tools and data you explicitly allow.

Clear activity record

Every action, decision, and system call is logged and searchable.

Ongoing performance checks

Behavior is tested as workflows, data, tools, and models change.

Where agents help first

Start where work is repetitive, system-heavy, and approval-aware.

The best starting points have clear decision logic, multiple system handoffs, and enough operational pain to justify a production workflow.

Customer operations

Case resolution

Problem

Teams spend time reading, sorting, and routing cases instead of solving them.

Agent

Reads requests, classifies issues, drafts responses, updates CRM records, and routes low-confidence cases.

Result: faster response and consistent case history.
Finance operations

AP/AR and close

Problem

Invoice matching and exception handling slow down the close and increase errors.

Agent

Extracts invoice data, matches POs and ledger entries, flags exceptions, and prepares approvals.

Result: faster invoice flow and cleaner audit log.
Risk + compliance

Decision support

Problem

Teams spend days collecting evidence before they can make a risk or compliance decision.

Agent

Gathers data, applies policy rules, and assembles decision packets with sources for approval.

Result: faster reviews and traceable outcomes.
Supply chain

Exception handling

Problem

Delays and disruptions are discovered late, forcing manual replanning across teams.

Agent

Monitors ERP and logistics signals, detects issues, proposes updated plans, and coordinates changes.

Result: earlier response and less manual coordination.
Customer stories

Proof points that make agentic AI feel practical.

These examples support the broader agentic message: AI systems create value when they are connected to real workflows, knowledge, and operations.

AI onboarding assistant

PetroLedger

Sphere built a generative AI onboarding platform that preserved expertise, sped up training, and turned knowledge retention into measurable savings.

120% faster ramp-up · $1.2M annual savings
Operational foundation

Software division carve-out

Sphere implemented NetSuite, HubSpot, and core operational integrations for a newly formed software company under a six-month deadline.

Finance, CRM, and integrations delivered under pressure
GenAI summarization

Ascentia

Sphere helped implement a generative AI solution to improve call summarization and customer service efficiency.

AI workflow improvement from workshop to proof of concept
Client voices

Delivery confidence matters when AI becomes operational.

Use concise testimonials to reinforce execution credibility without turning the page into a review wall.

“These things would not have been achievable if we did not build our own in-house system and partner with Sphere.”
Lee EbreoVP of Engineering · CreditNinja
“They keep knocking projects out of the park, including the rescue of a project another vendor had mishandled.”
Selah Ben-HaimVP Engineering · Prominence Advisors
“Sphere consistently prioritizes the needs of their clients, demonstrating both agility and teamwork.”
Mark FriedganCEO · CreditNinja
Choose your starting point

Bring one workflow. Leave with a delivery plan.

The page should give buyers a clear next step whether they have a workflow in mind, need help choosing one, or are ready to build.

Start here

Agentic fit call

Review your workflow shortlist, feasibility, systems involved, risk profile, and control points.

30-45 minWorkflow shortlist
Book fit call
Most useful

6-week agent delivery plan

Map target architecture, approval gates, first milestone, success metrics, and pod composition.

6-week planProduction path
Get delivery plan
Build

Embedded AI Foundry pod

AI/ML engineers, data and platform engineers, MLOps, RAG, and agentic AI architects build with your team.

4-7 day startWeekly visibility
Discuss pod
Questions before you start

Agentic AI, explained plainly.

Agentic AI refers to AI systems that can take actions across tools and workflows, not just answer questions. Production agents read data, execute tasks in connected systems, and route decisions to humans when approval is needed.

A production agent runs live workflows in your business environment with proper permissions, action logs, monitoring, governance controls, and approval points for high-risk decisions.

Sphere can start a team in 4-7 days. The production implementation follows a phased path: workflow mapping, build, reliability validation, and responsible scaling.

Yes, when designed properly. Agents operate with scoped permissions, use only authorized tools and data, log every system call, and integrate with existing identity and access controls.

Agents should include fallback paths and escalation rules. When confidence is low or unexpected conditions arise, the workflow routes to human review instead of proceeding unchecked.

No. Sphere designs portable systems that support multiple models, APIs, and platforms so providers can be swapped or tested without rebuilding the workflow.

Start with one workflow and build a production agent around it

Bring your workflow shortlist, systems, and risk constraints. Sphere will help map the agent, control points, target architecture, and first milestone.