Agents that stay inside the workflow.
Task-bounded AI agents for production workflows — not open-ended chat. Bounded actors with inputs, outputs, and a plan for when they fail.
The business problem
Demos do not equal operations
Unbounded autonomy creates risk
Support cost is the hidden bill
Task boundary matrix
What an agent may do, must never do, and when it escalates — decided before models enter the conversation.
| Dimension | In scope | Out of scope | Escalate |
|---|---|---|---|
| ActionsSide effects on systems | Approved tool contracts | Open-ended tool inventing | Unknown side effect |
| Data access | Scoped fields / tenants | Broad export or dump | Ambiguous residency |
| Confidence | Above threshold | Silent guesswork | Below threshold |
Orchestration with guardrails
Plan, tool, guard, act — then escalate
Agents earn trust through boundaries. Policy sits between reasoning and side effects; humans remain a designed path.
Tools are contracts
Each capability is scoped, versioned, and observable.
Escalation is designed
Uncertainty routes to operators — not silent failure.
How we build agents
Scope the job, wire the workflow
We constrain tools and actions, evaluate against real workflows, and integrate with systems operators already run.
Real inputs and outputs
Agents connect to production systems — not slide decks.
Evaluation before scale
Failure modes are tested against operational criteria.
Where agents land
Integrated into systems you own
APIs and permissions define the tool layer — so the agent is not stranded in a chat window.
Permissions first
Tool access mirrors operator roles.
Auditable write-backs
Every action leaves a trace you can review.
Scope an agent workload
Describe the workflow, the actions involved, and who supports it today.
How we deliver
Bound the job
Inputs, outputs, tools, and failure modes — written before any demo.
Wire and evaluate
Connect contracts, run against real workflows, measure confidence and escalation.
Ship with operators
Observability, rollback, and a support path your team can live with.
Why Choose Konic Labs for AI Agents
Task-bounded by design
Failure modes you can operate
Integrated, not isolated
Honest about limits
Related Case Study
Related Insights
AI Agents — FAQ
- How is this different from chatbots?
- Production agents take scoped actions inside workflows — with tools, policies, and evaluation. Chat is an interface; the engineering is in the boundaries.
- Can agents use our internal APIs?
- Yes — that is usually the point. We design tool contracts around what your operators already trust.
- What if the agent is wrong?
- We design for it. Confidence thresholds, human escalation, and audit trails are part of every engagement — not optional extras.
Scope an agent workload
Describe the workflow, the actions involved, and who supports it today. We will be direct about whether an agent is the right tool.