AI agent development services for reliable business automation.
An agent is useful when it can finish a task, and trustworthy when a person can stop it. We design the tools, retrieval, evaluation, and escalation before tuning the first prompt.
- Safety checks
- 04
- Before launch
- Tested
- Sign-off required
- Human
- Plan included
- Rollback
Overview
What AI Agents & Assistants has to solve.
A workflow where judgement is useful and consequences need human approval. The useful questions are about ownership, failure paths, and what the team can operate after launch.
What good looks like
Every action has a named owner We measure quality before launch Irreversible steps wait for a person Tools have clear scopes and permissions The agent can be stopped and reversed
Scope is a safety feature
An agent with unlimited tools becomes less predictable. The useful design question is what the workflow needs the model to do, with which evidence, and under whose authority.
We start with the decision and the consequence. Read-only tools come first. Irreversible actions need explicit approval. The agent gets a narrow surface that can be tested, traced, and explained.
- Typed tool inputs and outputs
- Permission-aware retrieval
- Read-only before write access
- Human approval for irreversible actions
Evaluation comes before autonomy
A prompt that works in a demo proves little. We build a representative evaluation set from real cases, define what a good answer looks like, and measure the agent before we expand its scope.
The evaluation becomes the release gate for the next tool, prompt, model, or retrieval change. The team can improve the agent without guessing.
- Representative cases and edge cases
- Scoring rubric and human review
- Regression checks on every change
- Shadow mode before production action
What a useful agent brief should settle
An agent brief should name the task, the evidence of completion, and the actions the agent may take without review. It should also say what happens when the model is uncertain or unavailable.
Define the documents and systems the agent can read, the tools it can call, and the audit record it leaves behind. Add autonomy after the evaluation shows the workflow is reliable.
- Task, owner, and definition of done
- Allowed tools, actions, and approval gates
- Retrieval sources, permissions, and retention
- Evaluation cases, monitoring, and rollback
Why teams call us
What usually goes wrong.
These are the recurring problems behind a ai agents & assistants brief. Naming them early changes the architecture, the estimate, and the launch plan.
The agent has too many tools
It can read everything, write everywhere, and call any API. The team cannot explain what it might do next.
There is no evaluation set
The demo looked good on five examples. Nobody knows how it handles the cases that matter or how it fails.
Retrieval pulls the wrong content
The assistant retrieves stale or unauthorised content because nobody defined freshness, permissions, or source quality.
Nothing stops the agent
The agent can send, refund, or approve without human review. One bad output can become an incident.
Scope
What the project includes.
Every ai agents & assistants project is scoped around a working system and the decisions that keep it healthy.
Agent and tool design
A limited set of tools with typed inputs, permissions, and clear success and failure behavior.
Retrieval and context policy
Source selection, freshness rules, permission-aware retrieval, and a context budget the model can use.
Evaluation suite
A representative test set, scoring rubric, regression checks, and a release gate tied to quality.
Human approval and escalation
Approval queues for consequential actions, evidence for the reviewer, and a clear escalation path.
Observability and rollback
Step-level traces, cost and latency dashboards, error visibility, and a documented stop and reverse path.
Production handover
Runbooks, model and tool ownership, evaluation maintenance, and a roadmap for expanding autonomy.
Use cases
Where this service helps most.
The same service looks different in each company. These are the situations where it matters most.
An operations assistant needs to act
The team wants an assistant that can look up an order, check policy, draft a response, and route an exception without a person copying data between systems.
A scoped assistant with evidence and approval
A support team is buried in research
Agents spend too long finding the right policy, customer history, and next action. An assistant could assemble the context, but it must not invent an answer.
Permission-aware retrieval with citations
A back-office decision is slow
The decision requires judgement across several documents and systems. The team wants a recommendation with evidence and a person to sign off.
A recommendation workflow with human sign-off
Process
Four stages. Working software at every review.
Discovery leads to an architecture decision, a build with visible increments, and an operating model that stays after we leave.
What stays constant
One accountable lead, a working demo at each review, and documentation from the first release. Scope and risk set the plan, and we will say when a smaller project is the better answer.
- 01
Scope
Map the workflow, the decision, the tools, and the consequences. Define what the agent may do and what must stop for a human.
Output: Agent brief, tool map, risk register
- 02
Build the evaluation
Collect representative cases and define the scoring rubric before tuning the prompt or connecting production tools.
Output: Evaluation set, baseline, quality bar
- 03
Pilot
Build the smallest end-to-end agent with real data, approvals, and traces. Run it in shadow mode where possible.
Output: Working pilot, evaluation report, go/no-go
- 04
Harden
Add permissions, retries, monitoring, and rollback. Expand the tool surface only after the quality gate is stable.
Output: Production agent, runbook, monitoring
Stack & standards
Chosen for durability.
Fewer moving parts, clear ownership, and a handover your engineers can defend. The stack follows the constraint.
OpenAI / Anthropic
Model choice matched to task and risk
LangGraph
Explicit agent state and tool boundaries
pgvector
Permissioned retrieval over owned content
PostgreSQL
Audit, approvals, and state
OpenTelemetry
Step-level traces and cost visibility
How we choose
Agent versus workflow
Use an agent when the path is not fixed and judgement is required. Use a workflow when the steps are known, auditable, and cheaper to run as fixed rules.
Assistant versus autonomous agent
An assistant drafts, recommends, and gathers evidence. An autonomous agent acts. Start with the assistant, measure quality, then add autonomy where the risk is understood.
Build versus platform
A platform is a sensible start for a common support or research workflow. Custom work makes sense when the tools, permissions, or evaluation criteria are specific to your business.
Measured outcomes
“They earned trust before they asked for it. The model shipped in shadow mode, and the technicians were the ones who promoted it.”
FAQ
Questions we hear often.
The questions buyers ask before a ai agents & assistants project. If yours is not here, ask it directly.
Ask an engineer01 How do you stop an AI agent from taking a wrong action?
We limit the tools, require approval for irreversible actions, log every step, and keep a rollback path. The agent gets write access only after the evaluation and permission model support it.
02 Can we start with a narrow workflow?
Yes. We scope one task with a clear owner, a definition of done, and a safe approval path. The pilot proves the evaluation and operating model before the agent receives broader tools or autonomy.
03 Can the agent use our internal documents?
Yes, with permission-aware retrieval and freshness rules. We define which sources are allowed, who can see them, and how the agent cites the evidence it used.
04 Do we need a large language model?
Sometimes. If the task follows fixed rules, a rule or smaller model may be cheaper and more reliable. We choose the simplest approach that meets the quality bar.
05 How do you measure quality?
We build an evaluation set from real cases, define a rubric, and measure accuracy, completeness, groundedness, and safe failure. The same evaluation becomes the release gate.
Next step
What should an agent be allowed to decide?
Tell us about the workflow, the evidence, and the consequence. A senior engineer will reply with a scoped agent and evaluation plan.
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