Job
Name the Outcome First
Qualify a lead, draft a reply, update a record. One job with a finish line beats an agent that “helps with everything.”
Scope
Tight
AI Agents
AI Agents That Do a Defined Job — Then Stop
Custom AI agents for lead qualification, ticket drafts, record updates, and internal checklists — with tool access you approve, logs you can read, and a human in the loop when the action cannot be undone.
What a production-ready AI agent needs:
One Clear Job
Tool Access
Human Approval
Sandbox Tests
Full Logs
Live Monitoring
Models we use when an agent needs to reason and then act
OpenAI
Anthropic
Claude
Gemini
Mistral
Perplexity
Midjourney
OpenAI
Anthropic
Claude
Gemini
Mistral
Perplexity
Midjourney
Agents that act in your tools — with a stop button you control.
springsol · agent studio
Choose the agent layer you need to ship next.
Scoping, tools, multi-step workflows, evaluation, or live operations — see how SpringSol can put an AI agent to work without giving it the keys to everything.
Scope
Define the Job the Agent Should Own
An AI agent is useful when it has a bounded job: qualify a lead, draft a reply, update a record, or run a checklist. We start by naming that job, the tools it may use, and when a human must approve.
Open-ended “do everything” agents fail. Narrow, testable ones ship.
Task and outcome workshops
Tool and data access list
Human approval checkpoints
Success metrics you can measure
Tools
Connect CRM, Email, Docs & Internal APIs
Agents only help if they can act in the systems you already use. We wire CRM, helpdesk, calendars, and internal APIs with the least privilege needed for the job.
Each action is logged so you can see what the agent did — and undo it if needed.
CRM and helpdesk actions
Email and calendar tools
Internal API and database hooks
Audit logs for every step
Orchestration
Multi-step Agents With Clear Stops
Some work needs more than one step: read a ticket, look up the order, draft a reply, wait for approval, then send. We design those sequences so the agent does not skip a check or loop forever.
Timeouts, retries, and “ask a person” paths are part of the design.
Step-by-step agent workflows
Approvals before irreversible actions
Retries, timeouts, and fallbacks
Handoff to the right teammate
Docker
Quality
Evaluate Before You Trust It Live
We test agents on real past tickets and edge cases — not a handful of happy-path demos. Wrong actions are cheaper to catch in a sandbox than in production.
You see sample runs, failure modes, and what we changed before go-live.
Sandbox runs on historical cases
Accuracy and safety checks
Edge-case and abuse testing
Go-live criteria you agree in advance
Operate
Monitor, Retrain & Expand the Job
Once live, we watch error rates, approval queues, and time saved. New tasks are added only when the current job is stable.
Agents are software. They need owners, logs, and a plan for model or API changes.
Live dashboards and alerts
Prompt and tool updates
New tasks after the first job is stable
Support retainers for model changes
Support
Agent questions?
What we will and will not do.
How is an AI agent different from a chatbot?
A chatbot answers. An agent can take steps in your tools — create a ticket, update a CRM field, draft and queue an email. That is why we scope the job, limit tools, and require approval for actions you cannot easily undo.
Can it work inside our CRM and helpdesk?
Yes, when those systems have APIs or supported connectors. We connect only the actions the job needs and log each one. If a system cannot be automated safely, we say so.
How do you stop it from doing the wrong thing?
Sandbox tests on real historical cases, allow-lists for tools, timeouts, and human checkpoints. We also monitor after go-live. No agent is risk-free; the design is about making mistakes visible and reversible.
How long does a first agent take?
A narrow first job — for example drafting support replies for one queue — is often weeks, not quarters. Broader multi-system agents take longer because evaluation and access reviews take time. You get a plan after we see the job and the tools.
Who owns it after launch?
You do, with our help if you want a retainer. Agents need someone to watch queues, update prompts, and decide when to add the next task. We will not leave a live agent with no owner.
Start small
Give an agent one job worth doing
Tell us the task, the tools it would need, and what a person must still approve. We’ll propose a first agent you can test on real cases before it touches production.