AI agents that still work in the third month
You get AI agents that act inside the systems you already run, and a team that stays on to keep them right after launch.
- 4.8 on Clutch across 22 reviews
- PremiumAudit.io: audit cycle cut 75%, calculation errors cut 95%
- In production for SmartREI, Tankaware, ZyloBase, KiwiWrite
A demo proves an agent can work once. Production asks a harder question: can you tell, three months later, that it is still doing the job? That is a different build. It needs scored evals before release and permissions capped per tool. It also needs a trace of every decision you can replay, and someone whose phone rings when it breaks.
Agents fail quietly, which is the expensive way
A script fails loudly. It throws, the run stops, someone gets paged. An agent picks a defensible wrong action instead, finishes, and logs a success. Nobody notices until a customer does.
RAND looked at why AI projects die. The leading cause is not the model. It is teams getting the problem wrong: a model tuned for the wrong metric, or dropped into a workflow it does not fit. Their report also cites estimates that more than 80% of AI projects fail, roughly twice the rate of IT projects without AI. RAND presents that number as an outside estimate rather than its own measurement.
So the first question is what the agent must never do alone. Your answer shapes everything after it.
We published a ranked comparison of the firms in this space, ourselves included and not at the top. Read it before you shortlist anyone.
What we actually build
Named systems, not capability nouns. Each of these runs for a real client.
Agents that answer the phone
ZhoopZhoop's receptionist takes live customer calls over Twilio and books work while every tech is under a car. Outbound agents ring suppliers for parts quotes.
Agents that read documents
For PremiumAudit.io we built insurance premium audit parsing on Claude, with field mapping, validation and exception handling, and a human auditor still in the loop.
Pipelines that route the work
Little Tree Confections turns every Fireflies meeting transcript into department-routed ClickUp tasks and Notion docs, through n8n. Fixed steps, a model inside them.
Tool access without the keys
Go4Gr8 uses MCP in production so a leadership coaching platform can track commitments through tools, scoped to what each agent is allowed to touch.
- DiagnoseOne workflow. Agree what done means.
- ScopeTools, and what needs a human.
- EvaluateScored cases before anything ships.
- ShipInto the live workflow, not a sandbox.
- WatchTraces, drift checks, someone on call.
The Evaluate node is where most projects skip straight to Ship. It is also the only node that tells you a prompt change made things worse.
Deciding where the model is not allowed to decide
Give a model twelve tools with overlapping names and it starts choosing by vibe. Fewer tools with sharper edges win every time.
So each tool gets one job, narrow arguments, and the least permission that still does the work. Anything you cannot undo waits for a person. You get that line drawn in the first week, because retrofitting it after launch means rewriting the agent. In insurance automation that line is binding: the agent reads and quotes a submission, and a licensed producer binds it.
- One job per tool, with a schema that rejects bad arguments before your database sees them.
- A stop rule on steps, spend and time, which escalates instead of looping.
- Every call logged with its arguments and result, so any past run can be replayed.
- Irreversible actions gated behind a human, always.
What we have shipped
22
production systems delivered
4
of them agent or AI automation builds
1
using MCP in production: Go4Gr8
60
days' notice Anthropic gives before a model retires
What we build on
Models and protocols
- Claude API
- OpenAI function calling
- MCP
- Deepgram Voice Agent
Build
- Python
- FastAPI
- React
- TypeScript
- Celery + Redis
- PostgreSQL
Run
- AWS
- Docker
- Sentry
- GitLab CI
- n8n
- Make.com
Integrations shipped
- Twilio Voice
- Twilio WhatsApp
- ClickUp
- Notion
- Fireflies.ai
- SendGrid
Running for real clients now
ZhoopZhoop
AI receptionist and parts procurement for auto repair.
Read the case study →
Go4Gr8
Custom AI sparring-partner platform for leadership coaching.
Read the case study →
Little Tree Confections
n8n + AI meeting-to-action automation for a bakery.
Read the case study →
Cruise Search AI
AI cruise discovery for military and veteran travelers.
Read the case study →
“I am extremely happy with the results and would highly recommend Hashlogics to anyone.”
Daniel Khin · CEO, PremiumAudit.io
The usual agent project against ours
Both ship something that works in a demo. They part company at the handover.
Proof it works
The usual approach
A demo run by the person who built it.
How we build
A fixed set of scored cases, re-run on every change.
Permissions
The usual approach
One key with broad access, because it is faster.
How we build
Least privilege per tool. Irreversible actions need a person.
When it misbehaves
The usual approach
Logs show the run finished.
How we build
Replay the run: inputs, tool calls, arguments, results.
Agent or workflow
The usual approach
Whatever was sold. Usually an agent.
How we build
We say when rules are the cheaper, safer answer.
After launch
The usual approach
The contract ends at go-live.
How we build
An agreed service level, or a documented handover to your team.
Model retirement
The usual approach
Discovered when the agent starts erroring.
How we build
Planned. Model id in config, test set ready to prove the replacement.
Bring us the workflow, not the idea
Scoping calls are free. Every build starts with a fixed-fee Blueprint: an engineer reads your real systems and writes the plan, with a fixed price for each milestone rather than a guess. The fee is credited in full against the build, and if the plan isn't one you'd act on, you don't pay for it.
01How do we know an AI agent will actually work for our process?+
You do not, until it is measured against your real cases. That is why the first thing we build is the scoring set rather than the agent. If the scores say rules would do the job, we tell you that and the project gets smaller.
02What does it cost to build an AI agent?+
Cost is driven by how many systems the agent touches, how much of the work is exceptions, and whether any evaluation data exists yet. Scoping calls cost nothing. Every build then starts with a fixed-fee Blueprint, credited in full against the build, and if the plan isn't one you'd act on, you don't pay for it. You get a fixed price after that, so the scope is settled before the build starts.
03How is an AI agent different from RPA?+
RPA follows rules a developer wrote in advance and gives the same result every run. An agent decides at run time. That is what lets it read a phone call or an odd document layout. It is also why it can be wrong in a new way. We build both and say which one your process needs.
04Who maintains the agent after it goes live?+
You choose at the start, not after launch: an agreed service level with us, or your own team trained and handed the documentation. The first 2 months of support and maintenance are free, with every build. An agent with no owner drifts quietly as inputs and models change.
05What happens when the model our agent runs on is retired?+
We migrate it, and the plan exists before you need it. Anthropic publishes retirement dates and promises at least 60 days' notice for a public model, so the model id lives in configuration and the scored test set proves the replacement before the deadline.
06Can an agent work with the tools we already use?+
Yes, through their existing APIs, and that is usually cheaper than any migration. The Little Tree Confections pipeline reads Fireflies transcripts and writes into ClickUp and Notion with no manual re-entry.
07Do AI agents replace our staff?+
No. They take the repetitive, judgement-light part of a process and escalate the rest. That is why every build we ship has a defined path to a human. ZhoopZhoop's agents answer calls that were previously missed while the team was working on cars.
08We are a startup. Is this only for enterprises?+
No, and the shape differs. A startup usually wants one workflow automated and evidence it works within weeks; an enterprise wants multi-site rollout, audit trails and permissioned access from day one. Both get the same evaluation and tracing, because both fail the same quiet way without it.
Related
- AI agent →What an AI agent is, and where the line with a workflow sits.
- Best AI agent development companies →The signals that separate a production agent from a demo, tested against us too.
- Best text-to-speech APIs →How we picked the voice layer for a phone agent in production.
- From AI prototype to production →What runs after the demo.
- Hire AI agent developers →Engineers who have been on call for one.
