AI agent development
AI agent development for workflows that need judgment
We start with the business job, not with the assumption that it needs an agent. Where the work is predictable, ordinary automation is cheaper and steadier. Where it needs interpretation, decisions and action across several tools, we build an agent: bounded permissions, and a person signing off where it matters.
Definition
What an AI agent actually does
Less than the marketing suggests, and more than a script.
- 01
Reads the situation
An email, a document, a record or a request, usually unstructured and often incomplete.
- 02
Decides the next step
Within rules you set: classify it, look something up, ask for what is missing, or hand it to a person.
- 03
Acts through tools
Updates the CRM, drafts the reply, files the document or raises the ticket, using only the actions it has been given.
- 04
Checks, then escalates
Confirms the result, records what it did and why, and stops to ask when a case falls outside its limits.
A chatbot waits for questions. An agent is given a defined job and completes it within limits you can read.
Agent or automation
When you need an agent, and when you don't
Many processes we are asked to make “agentic” don't need it. Saying so is part of the audit.
Ordinary automation is enough when
- Inputs arrive in a predictable format
- The same rules apply on every run
- Exceptions are rare and can simply be flagged
- You need it cheap, fast and identical every time
An agent earns its place when
- Inputs are unstructured: emails, PDFs, free-text requests
- The right next step depends on what the input says
- The job crosses several systems, with lookups between steps
- Exceptions are common, and handling them is the real work
Use cases
Where agents earn their place
Defined jobs with a clear finish line, not open-ended assistants.
- Intake and classification: read inbound emails and documents, work out what each one is, extract what matters and route it to the right queue or person.
- Research and decision support: gather what is known about an account, a supplier or a request from several sources, and prepare a recommendation for someone to approve.
- System updates: keep the CRM and internal records current from what arrives in inboxes and forms, with every change logged.
- Multi-step operations: work through a process that crosses several tools, completing the routine cases and parking the unusual ones for review.
- Approval and escalation: prepare the decision, apply your thresholds, and send anything above them to the right person with the context attached.
- Retrieval, then action: find the answer in your own documents, then act on it by drafting the response, filling the form or opening the ticket.
How we scope an agent
The free AI Ops Audit maps one workflow as it actually runs. We write down the job, its inputs, the decisions inside it, the tools it may touch and where a person must sign off. If ordinary automation would do the job, the plan says so. Scope and price are fixed before anything is built.
30 minutes on one workflow. The plan says plainly whether it needs an agent.
Control
Human control and reliability
An agent you cannot watch or stop is not one you should run.
Bounded actions
It can do only what it has been permitted to do: named tools, named records, nothing open-ended.
Approval where it matters
Anything costly, irreversible or customer-facing can wait for a person to approve it before it happens.
Every step on record
Each decision and action is logged with its reason, so you can see what it did and why.
Fallback, not guesswork
When it is unsure, or a step fails, it stops and hands the case to a person rather than pressing on.
Evidence
Relevant systems experience
What decides whether an agent can be trusted is grounding in real data, judging quality, and falling back when results are weak. These systems were built around exactly those problems.
Built at Koenig Solutions
Prior professional experience. Our founder built these systems as an employee at Koenig Solutions, who own them.
Grounded retrieval at library scale
Retrieval across 8,000+ courses, feeding a generation pipeline that produced 350,000+ practice questions drawn from that syllabus, each quality-checked before publication. The discipline carries over directly: output tied to source material, and checked before anyone relies on it.
Built and run by Ergara
The Networking Contact Finder
A free public tool on this domain that works through a fixed sequence: read a job posting, extract the company and role with a model, search for relevant people, then score each match. It returns fewer results rather than padding weak ones, attempts to confirm a guessed company domain against the posting or a web search, and runs a differently phrased search when the first finds nothing.
What you own
What you are actually buying
You'll own it
Built in your accounts, on your credentials, documented. If you stop working with us, nothing stops working.
Scoped before it's built
Nothing open-ended. The audit fixes the scope and the number before a single thing gets built.
Related
Related services and tools
Broader custom AI: retrieval over your documents and generation at volume, where no decisions are handed to the system.
Predictable, rules-based processes that should simply run on a schedule.
The tool described above, free and with no sign-up.
Questions
About AI agent development
Do we actually need an agent?
Often not. If the steps are the same every time, ordinary automation is cheaper, faster and easier to trust, and the audit will recommend it. An agent is worth its extra complexity only where the work depends on reading and judging inputs that vary.
Will it make decisions without us?
Only the ones you agree it may make. We set out in writing which actions it takes on its own, which wait for approval, and which always go to a person. High-stakes decisions stay with your team.
What happens when it gets something wrong?
It is built on the assumption that it sometimes will. Low-confidence cases go to a person instead of being guessed, every action is logged so a mistake can be traced and corrected, and behaviour is tested against your own examples before it goes live and whenever it changes.
Is this the same as a chatbot?
No. A chatbot waits to be asked. An agent is given a defined job, such as working through an inbox or keeping records current, and completes it within limits. If what you need is staff asking questions of your documents, that is a retrieval system, which sits under custom AI development.
Which models and tools does it run on?
Whatever suits the job and the systems you already use. The model sits behind an interface, so changing provider later is a planned change rather than a rebuild, and everything runs in your own accounts, billed to you directly.
Find out what you shouldn't be doing.
30 minutes, no pitch, and a written plan you keep either way.
Get your free AI Ops Audit