Bound the job
Define what the agent may decide, what it may change, and where a human must remain responsible.
The Agency
Agent and LLM Systems
Devzilla designs agent and large language model systems with explicit tools, retrieval, memory, evaluation, cost controls, and human escalation.
Direct answer
What makes an AI agent reliable enough for production?
A production agent needs bounded responsibilities, typed tools, explicit permissions, observable state, repeatable evaluation, cost and latency limits, and a safe escalation path. Devzilla treats the model as one component inside that operating system rather than asking an unconstrained prompt to carry the entire product.
When it fits
Situations that justify the work
- A chat demonstration needs to become a workflow that reads and changes real systems.
- Retrieval quality varies and nobody can explain which source produced an answer.
- Long-running tasks need state, checkpoints, retries, and human review.
- The team needs to compare models or prompts without rebuilding the product around each test.
Outputs
What the client receives
- Tool contracts, permission boundaries, and orchestration for bounded agent workflows.
- Retrieval and memory architecture with visible source provenance.
- Evaluation harnesses for task success, tool behavior, hallucination, latency, and cost.
- Tracing, model routing, fallback paths, and human escalation controls.
Approach
How Devzilla approaches the system
Make tools explicit
Use typed inputs, outputs, permissions, and idempotent behavior wherever the underlying system allows it.
Evaluate complete tasks
Measure whether the workflow reaches a correct outcome, including tool use and recovery, instead of grading isolated prose.
Operate with evidence
Trace model calls, tools, retrieval sources, cost, latency, retries, and escalations so failures can be reproduced.
FAQ
Frequently asked questions
Does every AI workflow need multiple agents?
No. Multiple agents add coordination and failure modes. Devzilla starts with the smallest architecture that can complete the bounded task and adds roles only when evaluation supports the complexity.
How do you evaluate an agent?
Evaluation covers task completion, factual grounding, tool selection, argument correctness, permission boundaries, recovery behavior, latency, and cost across repeatable scenarios.
Can an agent use a company knowledge base?
Yes. Retrieval or graph-backed memory can expose approved company knowledge, but the design must preserve provenance, access control, freshness, and a way to decline when evidence is missing.
Does this match the hard part?
Tell us what the system must do, what it touches, and what failure costs.
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