Get the repetitive work off your desk.
Document intake, internal search, reporting, and the handoffs between your tools. Pick one useful workflow, build it, and check whether it saves your team time.
Tell us about your workflow
PRODUCTION AI ENGINEERING
AI systems.
AI systems.
Built to
hold up.
We find what drives your agent costs, trace what fails, and engineer the controls production demands.
20 minutes with the Agen2ic team. A small first step, no project commitment.
Explore the engineering tipsSYSTEM / 001AUTOMATIC WALKTHROUGH
RequestINPUT
AgentORCHESTRATION
Tool accessLOAD ON DEMAND
Evaluation gateCHECK THE OUTPUT
ResponseOBSERVED
01 / CONTEXT OVERHEAD
01
Every token has a source.
Load tool definitions when they are needed. Attribute spend to the call that caused it.
FOLLOW A REQUEST
$520 MINUTES
THE FIRST CONVERSATIONCover my coffee.
Cover my coffee.
Let’s look at your problem.
Bring a question, a rough idea, or the task your team is tired of doing by hand. Meet with the Agen2ic team, talk it through, and decide where to go next. Twenty minutes, five dollars, and no commitment to a larger project.
Engineering experience behind Agen2ic.
AWS Dedicated Cloud · SLAXN · Clemson Advanced Computing
$3.4MAnnual cost savings
AWS automation
AWS automation
36,000Engineering hours saved
AWS automation
AWS automation
1.405 PFClemson supercomputing
2,000+ servers · 8+ PB
2,000+ servers · 8+ PB
9 yearsInfrastructure practice
2017–present
2017–present
01 / WHAT WE CAN BUILDSTART WITH THE WORK THAT NEEDS DOING
Where would a better
system help?
You don’t need an AI roadmap to start a conversation. Show us the work that takes too long, the prototype you want to launch, or the infrastructure that keeps getting in the way.
Build something you can depend on.
Cloud architecture, migrations, Kubernetes, automation, and the monitoring that makes operations manageable. Bring an existing system or plans for a new one.
Talk through the infrastructureGet the demo ready for real use.
Find the expensive calls, test the failure paths, and put limits around what an agent can do. Leave your engineers with traces, evaluations, and a system they can operate.
Explore the readiness audit02 / FROM THE FIELDA $15 estimate.
A $15 estimate.
A $248 lesson.
Seventy-two skills were loading into context on every call. Prompt caching wasn’t working on another part of the job, and the cost monitor wasn’t running.
That changed how we build. Measure a small run first, put a hard limit on spend, and check that the monitor actually catches a problem. The diagram shows one part of that fix: loading skills only when needed.
Read the field noteCONTEXT WINDOW72 AVAILABLE SKILLS
Loaded into context Available, not loaded
72 / 72
This request needs 3 skills. Loading all 72 brings the other 69 along for the ride.
ENGINEERING TIPSSMALL CHANGES. FEWER SURPRISES.
A few things worth
learning early.
Practical lessons from building and operating AI systems. Pick a situation, try a change, and see the idea at work.
01 / CAPACITY
More workers can mean more waiting.
When jobs hit the same rate limit, extra workers can spend their time retrying. A queue keeps the work moving without flooding the service.
Try this on your system
Increase concurrency one step at a time. Measure completed jobs and retries, then stop adding workers when useful throughput stops improving.
ILLUSTRATED EXAMPLE
All twelve jobs compete for the same service.Three jobs run. The others wait their turn.
02 / EVIDENCE
Agreement can hide the same mistake.
Three reports can all read the same stale file. A useful second check looks at something independent, such as the output people actually receive.
Try this on your system
Introduce a known defect and check whether your test catches it. Trace where each report gets its evidence before treating agreement as confirmation.
ILLUSTRATED EXAMPLE
Three green reports. All reading one source.The output check catches what the reports missed.
03 / RECOVERY
An interruption shouldn’t erase the work.
Long jobs get interrupted. Save completed results as you go, and make restarts skip work that already succeeded.
Try this on your system
Stop a small batch halfway through and restart it. Check that finished items stay finished and that messages, payments, or writes are not repeated.
ILLUSTRATED EXAMPLE
The connection drops. The batch starts over.Saved results stay put. The batch resumes at item five.
04 / TOOL CHOICE
Some jobs just need a script.
Use a model where judgment helps. Let code handle counting, validation, and repeatable steps. Fewer model calls mean fewer tokens to pay for, and a workflow that’s easier to test and run.
Try this on your system
Write down which steps require judgment. Keep those model calls, and move arithmetic, file handling, and known rules into ordinary code.
ILLUSTRATED EXAMPLE
The model decides and calculates at every step.Code handles the fixed steps. The model reviews the content.
03 / A CLEAR WAY FORWARDONE WEEK. WRITTEN FINDINGS.
AGENT READINESS AUDIT / BESPOKE ENGAGEMENTFind the problems.
Find the problems.
Decide what to fix.
A dedicated engagement led by our principal architect, built around your system and the decisions ahead of you. We examine the code, trace the failures, and deliver findings your engineering team can act on.
Built around your system.Dedicated senior engineering time.
Individual scope and proposal after an initial conversation.
Individual scope and proposal after an initial conversation.
- Architecture and trust-boundary map
- Cost attribution and failure-mode findings
- Fix list prioritized by effort and impact
- 60-minute readout with your engineering team
AGEN2IC / ENGINEERINGDELIVERABLE PREVIEW
01 / SYSTEM REVIEWAgent readiness
Agent readiness
findings.
Sample findings
Request→Agent→Tools
P1
Unbounded retry path
Set a retry budget and timeout. Trace exhausted attempts to the originating request.
P2
Unattributed tool overhead
Record definition tokens per call. Load the relevant tool set on demand.
KEEP
What already works
Document the controls to retain, and the evidence supporting that decision.
FINDING → EVIDENCE → ACTION01 / 05
- DAY 01Map
Read the code and follow the calls.
- DAY 02Measure
Attribute cost to agents and tools.
- DAY 03Stress
Inspect timeouts and failure paths.
- DAY 04Evaluate
Check how correctness is measured.
- DAY 05Prioritize
Deliver findings and the fix list.
04 / TECHNICAL LEADERSHIPEngineering depth.
Engineering depth.
Practical judgment.
Garrett GallowayPrincipal Architect · Denver, Colorado
Our work connects the model, the servers underneath it, and the systems people use every day.
Garrett Galloway, our principal architect, trained stop-sign detection ML for the Ford CU-ICAR Deep Orange autonomous car project and worked on Clemson’s Palmetto supercomputer. He later co-founded SLAXN and led hybrid-cloud architecture and development.
His AWS Dedicated Cloud work spans automation and operations supporting twelve services, along with engineer training and multi-team technical presentations as an Amazon Associate Speaker.
We bring that experience to the design, explain the tradeoffs, and work with your team to get the system running.
Email us05 / BEFORE WE TALK
Before we talk.
A few things to know before the first call.
What do we cover in the $5 call?
Twenty minutes to talk about what you’re trying to do, what gets in the way, and where our team can help. It’s a small first step with no commitment to a larger project.
If you’d rather start by email, send us a few sentences about the problem.
We haven’t started using AI. Can you help?
Yes. We can start with one process your team repeats, look at the tools and data involved, and work out whether AI or ordinary automation would help. The first step is understanding the work.
We're pre-production. Is this too early?
No. Before launch is a good time to test the design, measure costs, and work through what happens when a call fails.
A smaller codebase also makes it easier to inspect the design before it becomes expensive to change.
We're on Bedrock, Vertex, or OpenAI directly. Does that matter?
We can work with any of those. We’ll look at how your application calls the model, uses tools, handles failures, and checks the output.
Your provider affects pricing, available controls, and where data goes. Those details are part of the review.
Will you sign an NDA?
Yes. Send it over before the intro call and we’ll sort it out before discussing sensitive details.
What if our problem isn't agents?
Cloud infrastructure, migrations, and automation are part of the work too. Tell us what you need running and we can scope it from there.
Can you work alongside our existing engineers?
Yes. The audit includes a 60-minute readout with your engineering team, and larger projects use your repo and review process.
We document what changes and walk the team through it so they can operate it after the engagement.
What if the audit finds we're basically fine?
Then we’ll say so. You still get the findings and a readout with your team.
The report includes what’s working and what we would leave alone.
FEDERAL CAPABILITY
View capability statement Engineering for accredited environments.
Principal holds an active TS/SCI with polygraph.
MET AT A CONFERENCE?Let’s pick up
Let’s pick up
where we left off.
Tell us where we met and what we were talking about. A half-formed idea is plenty to start with.
Send us a noteSOMETHING TO TAKE WITH YOU
Five checks before you put an agent to work.
A short guide to budgets, realistic tests, human approval, failure recovery, and checking the result where people will use it.
Read the checklist Or book a $5 problem review ↗Twenty minutes.
Five dollars. Your idea.
Bring the thing you want to figure out. We’ll help you find a useful next step.