'%20stroke='%2358585A'%3e%3cpath%20d='M2%200h34.52C65.57%200%2081%2022%2081%2050s-15.43%2050-44.48%2050H2Z'/%3e%3cpath%20d='m103.2%20100%2035-100%2035%20100'/%3e%3cpath%20d='M195.4%200h72m-35%200v100'/%3e%3cpath%20d='m287.6%20100%2035-100%2035%20100'/%3e%3c/g%3e%3cg%20transform='translate(490.36%200)'%20stroke='%23EF3E36'%3e%3cpath%20d='M2%20100V0l47%20100L96%200v100'/%3e%3cpath%20d='M118.2%200v73a36.5%2030%200%200%200%2073%200V0'/%3e%3cpath%20d='M298.6%2050a42.5%2053%200%201%201-21.25-45.9M298.6%2050h-24.65'/%3e%3c/g%3e%3c/svg%3e)
Agents with a bounded action surface — not autonomy theater.
Mid-sized teams don't need AI that runs on its own. They need workflows where AI does the routing, drafting, and pulling-together — and a person (or a system rule) approves anything that actually changes a record. That's what we build: AI that can take an action, with a person approving anything that matters, clear limits on what it's allowed to do, a test set that checks it's actually right, and a full record of every step so problems are easy to track down.
What this means for you
If you want AI to take routine work off your team's plate without letting it loose on your business, this is about AI that does the routing, drafting, and research while a person still signs off on anything that actually changes a record.
An agent isn't a chat window with delusions of grandeur.
It's AI that chooses the right tools, runs them in order, and produces tidy, structured results the rest of your systems can act on. The real design question is what it's allowed to do — not how 'independent' it is.
We build these the way mid-sized teams actually need them: a small, well-tested set of things the AI can do (search, look up, draft, sort, route); clear limits on what it can change (usually nothing important without a person approving it first); tidy results checked against a fixed format; a test set that runs through the whole multi-step process, not just the wording of a single question; and a full record of every step, so when something goes wrong you can see exactly what the AI did. The AI is just the coordinator; the disciplines are the same ones good teams already use.
What separates a production agent from a demo loop.
These are the requirements that turn a clever AI demo into something your operations team will let near a customer.
Tight limits on what the AI can do
The AI has a small, deliberate set of things it can do — usually under a dozen — each clearly defined. No 'run any database command it likes'. No open-ended system access. Anything that changes an important record sits behind an approval step or a rule your security team has signed off on.
Tidy results, checked against a fixed format
Every action the AI takes and every answer it gives follows a fixed, agreed format, and is checked automatically — so it can't quietly produce garbled data that breaks the next step.
A record of every step, not just the final answer
What the AI did, what it was working from, how long it took, and what it cost — captured for every run, using MLflow, LangSmith, Arize Phoenix, or the equivalent built into your platform. When the AI goes off the rails, you can see exactly where.
Approval steps and a person in the loop where it matters
Anywhere the AI would change a CRM record, send a message to an outside party, move money, or do something it can't undo, there's an approval step — a person reviewing it, a policy check, or a fixed rule. The AI drafts; a person or a rule confirms.
A test set that covers the whole process, not just the wording
A test set built from realistic, multi-step scenarios that re-runs on every change to a prompt, a model, or a tool, scored on what the business actually cares about (how often it resolves the issue, how often it has to escalate, how often it's wrong). That test set is your agreement with the business about what 'working' means.
Governance, identity, and usage limits
The AI acts under its own tightly limited account, not as the user. Usage limits per customer, defenses against booby-trapped inputs trying to hijack it, sensitive details hidden in the records it keeps, and an audit trail on every action. The same care you'd demand of any powerful automated system.
The agentic patterns we ship most often.
We're cautious about this kind of AI — these are the patterns where the math works for mid-sized teams.
Internal research and summary assistants
The AI looks across your trusted document sets in several steps — choosing which sources to check, pulling the threads together, and producing an answer that shows exactly where it came from. Useful for internal help desks, regulatory research, and briefings for account teams. Mostly reading, so little can go wrong.
Triage, routing, and ticket enrichment
Incoming tickets, leads, or claims sorted, filled in with relevant background, sent to the right queue, and pre-loaded with a draft reply. The AI handles the routine 80%; a person handles the tricky 20%.
Drafting workflows with human approval
Renewal emails, plain-English summaries of policy updates, first-draft contract edits, response-letter templates. The AI drafts using relevant background and structured inputs; a person reviews, edits, and sends. Faster turnaround without taking the human out of the decision.
Mostly-reading operations helpers
Status checks across systems, account-health summaries, on-call troubleshooting helpers. The AI reads from many systems, pulls together a picture, and recommends an action — but the action is taken by a person or a fixed procedure, not by the AI.
Signals an agentic build isn't the right next move.
If two or more are true, the work is either foundation work or a different shape of automation.
The job needs the AI to change important records with no human check
We can build it, but when it goes wrong it goes wrong expensively. Unless the business clearly accepts that risk and has a way to undo mistakes, a fixed workflow — with AI doing only the soft steps — is usually the better answer.
There's no test set, and no plan to build one
Without a test set built from real scenarios, you can't catch it when a change to the model, the wording, or a tool quietly makes things worse. Building that test set is a core part of the work.
The underlying data foundation isn't there
AI pointed at an unsolved data mess just confidently makes more things up. If your documents aren't well-kept, your customers don't show up as one clean record, or your source systems disagree, the AI inherits all of it.
A simple fixed workflow would solve 90% of it
If the rule is just 'tickets with these words go to this queue,' don't build AI for it. We'll say so — and help you ship the simple version instead.
Three reversible phases.
Old workflow (manual or rules-based) stays live until the new one matches it on the metric the business cares about.
See the full engagement modelAgent readiness assessment
Quoted by scopeWe scope the use cases, review what the AI would be allowed to do, check whether the underlying documents and data are ready, and recommend which workflows suit this kind of AI, which suit a fixed workflow with a few AI steps, and which suit neither yet.
The toolset, the test set, and the first build
Quoted by scopeThe set of things the AI can do (each clearly defined, with limits and approval steps), the test set, the full step-by-step record-keeping, and the first build end-to-end. Built on LangGraph, AutoGen, or whatever's built into your platform, depending on your setup and security needs.
Rollout, scale, and enablement
Quoted by scopeA staged rollout, checking the AI keeps up with your current manual process at every step. More builds on the same toolset once the foundation is proven. Working side by side with your team on the written guides. Post-launch and ongoing support are available by quote.
The questions we get most often.
LangGraph, AutoGen, CrewAI, or platform-native?
Depends on where your security and monitoring already live. LangGraph is the clearest and easiest to troubleshoot — usually our default for multi-step workflows in production. The tools built into your platform (Databricks Mosaic AI Agent Framework, Snowflake Cortex Agents, Bedrock Agents) win when keeping the AI inside your own walls matters more than being able to move between platforms. We pick based on your actual work, not any vendor relationship.
How autonomous is too autonomous?
Our default rule: the AI doesn't change an important record without an explicit approval step or a fixed policy check. 'Approval' can be a person, a rules engine, or a second AI trained to flag problems — but never the same AI that drafted the action. That one rule removes the most expensive kind of failure we see in mid-sized rollouts.
What about prompt injection?
A real risk — 'prompt injection' is when booby-trapped text hidden in an email or web page tries to hijack the AI. It matters most when the AI reads from email, web pages, or anything supplied by outsiders. Defenses we put in place: cleaning the inputs, making the AI's own instructions outrank anything it reads, checking results against a fixed format, giving the AI only tightly limited access so a hijacked one can't steal data, and stress-testing it as part of the regular test set. Not a solved problem — but a managed one.
How do you measure if the agent is working?
Business results first (how often it resolves the issue, how much faster the work gets done, how often it has to escalate, how often it's wrong), test-set results second (how many scenarios it passes, whether its summaries stick to the facts), and running health third (speed, cost per run, how often each step succeeds). All three on a dashboard your team owns by the time we hand over.
How this shows up in the work we ship.
Case studies and writing from engagements where this thinking actually meets production.
Adjacent topics in AI enablement.
Want to know if this is the right next move for your team?
Take the free AI & Data Readiness Assessment, or schedule 30 minutes to talk through your situation. Either way, you'll leave with a clearer picture of where you are and what the next steps should look like.