Tool-calling models
OpenAI, Anthropic, and Google models, chosen per latency and quality constraint.
Services · 04
Custom AI agents with bounded autonomy
An enterprise AI agent is software that plans steps, calls tools, and updates state on your behalf within policies you define. Databotiq builds agents for support queues, internal ops, and revenue workflows where the cost of a wrong action is too high for a chat-only toy.
At a glance
- Practice
- Custom AI Agents
- Best fit when
- a team needs to act on systems, not only answer questions, with auditable side effects.
- Typical Rapid POC
- 14 days, fixed scope.
Problems we solve
The pains buyers describe to us first.
Chatbots answer text but cannot execute safe, idempotent actions.
Tool access is either too wide (risk) or too narrow (useless).
Teams cannot audit who did what when something breaks.
Evaluations stop at offline benchmarks instead of production traces.
Approach
Our approach.
We start with a policy matrix. Which tools exist, which arguments are allowed, which actions always require human approval, and which are safe within budgets. The agent runtime enforces that matrix. Models do not get raw API keys to improvise.
Technical depth
Failure modes we design around
- Runaway loops: max steps, max spend, and wall-clock timeouts.
- Ambiguous tools: narrow interfaces with typed parameters.
- Silent tool errors: structured error surfaces and user-visible receipts.
Tech (May 2026)
Named tools, not vague acronyms.
Specificity earns trust. The choices below reflect what we ship today, and they will evolve as new models and tools clear our internal evaluations.
Tracing and evals
Eval harnesses grounded in your ticket exports and redacted production transcripts.
Retrieval
Optional retrieval when answers must cite internal knowledge with ACLs.
Where this fits
Industries and roles we ship for.
Tier-1 support
Account lookup, billing actions, and escalation to specialists.
Internal ops
Provisioning, access requests, and runbook-driven remediation.
Sales ops
Enrichment and CRM hygiene with field-level write policies.
Case pattern
A tier-1 agent that resolves account and billing issues end-to-end
This pattern fits SaaS teams where tier-1 tickets repeat: invoices, seat counts, plan mismatches, and refund policy questions. The agent reads account state with least privilege, proposes actions within policy, and escalates when confidence drops or a human must approve money movement.
Read the case patternOutcome
What this means for you.
You ship automation that can act, not only talk, with guardrails your security team can actually review.
Further reading
Related practice notes and patterns.
FAQ
Questions buyers ask about custom ai agents.
Specifics on accuracy, deployment, integration, and the proof path. If something isn't covered here,ask us directly.
Are agents just fancy RPA?
They can share UI automation in hostile environments, but the core is policy-bound tool use with explicit state. That difference matters when audits and regressions hit.
How do you handle prompt injection?
Untrusted text never becomes instructions. We isolate tools, sanitize arguments, and use allowlisted actions. Retrieval is filtered by access control lists.
What does observability look like?
Trace IDs across tool calls, redacted transcripts for training bans, and dashboards for containment rate, escalation rate, and policy violations.
Can agents write to our CRM?
Yes, when you want that. We use field-level permissions and dry-run modes until metrics prove safety.
How do we roll out safely?
Start with shadow mode, then partial traffic with kill switches, then expand budgets as error budgets allow.
What is the proof path?
A Rapid POC on a narrow queue with side-by-side human baselines and a written go/no-go on expanding tool permissions.
See it on your data in 10 days.
We run a sandboxed Rapid POC so you can evaluate outputs, integrations, and risk before you fund production.