Agentic AI · Tool-use · Multi-agent systems

Agents that
actually do the work.

A demo agent that books a flight is impressive. An agent that handles 8% of your tier-1 tickets, end-to-end, without escalations? That takes infrastructure. We build it.

Build an agent with us See architectures
The hard parts

Why most agent projects stall before production.

We've shipped enough of them to know exactly which problems eat 80% of the runway.

Bounded autonomy

Where does the agent decide vs. ask a human? Wrong answer in either direction kills trust or productivity. We design the policy.

Tool reliability

Tools fail. APIs rate-limit. Auth tokens expire. Retries, idempotency, circuit-breakers — boring eng that makes agents production-grade.

Memory architecture

Short-term context, episodic memory, semantic memory. Vector + graph + relational, with eviction and PII rules.

Eval at trajectory level

Not just "did the answer match?" — did the agent take a sensible path? We score traces, not just outputs.

Cost & latency control

An agent loop that calls a frontier model 14 times per task is a budget killer. We bound depth, parallelize, and cache.

Permissioning & audit

What can each agent touch? Per-tool RBAC, scoped credentials, full audit trail of every action — required for any regulated domain.

Reference architecture

The shape of a production-ready agent.

Most teams arrive with a single agent loop. We help them evolve to the supervisor pattern below — fewer hallucinations, faster trajectories, defensible cost.

EntryUser · API · Slack · Webhook
Input guardrailsAuth · PII · injection detect · scope check
Supervisor / plannerLangGraph state machine · DSPy · custom router
Specialist · researchRAG · web · internal docs
Specialist · actionAPI calls · DB writes · tool use
Specialist · reviewCritic · self-check · LLM-as-judge
MemoryShort-term · episodic · semantic (vector+graph)
Tool registryMCP · OpenAPI · scoped credentials
Output guardrailsSchema · safety · faithfulness · cost circuit-breaker
ObservabilityOpenTelemetry traces · LangSmith · Arize · cost & latency · eval scores
Trajectory observability

Every agent run, fully replayable.

Distributed traces. Tool inputs / outputs. Token costs. Eval scores. Per-step latency. Captured automatically and stitched into one timeline — so when something goes sideways, you find the exact step in seconds.

  • OpenTelemetry-nativeStandard tracing protocol — works with Jaeger, Tempo, Datadog, or your existing APM.
  • Step-level evalsScore each tool call, each LLM call, each retrieval hop — not just the end-to-end answer.
  • Replay & debugRe-run any historical trajectory against a new prompt or new model to A/B before deploying.
  • Cost attributionBy tenant, agent, tool, model — to the request. Showback dashboards out of the box.
trace · refund-request
2.4s · $0.043 · ✓
▸ supervisor.plan 230ms · $0.004
↳ tool: get_order 180ms · $0.000
↳ tool: get_refund_policy 95ms · $0.000
↳ rag: policy_corpus.search 340ms · $0.001
↳ specialist.action: process_refund 680ms · $0.022
↳ specialist.review: critic 410ms · $0.009
↳ output_guardrail.schema 42ms · $0.000
Steps
7
Trajectory eval
9.4 / 10
Outcome
resolved
Patterns we've shipped

What "agentic AI" actually looks like
when it works.

Customer support

Tier-1 ticket resolver

Handles password resets, refunds, tracking, and FAQ — with full audit trail and human escalation on confidence drop.

38%Tickets auto-resolved
4.6/5CSAT (vs 4.2 baseline)
$0.04Cost per ticket
Software engineering

PR-review co-pilot

Reviews diffs against architecture rules, security policy, and team conventions. Posts comments, never blocks.

−42%Cycle time
3.1×Bugs caught pre-merge
94%Comment relevance
Data ops

Self-serve analyst

Plain-English questions over governed data, with column-level lineage and access checks per request.

Analyst capacity
0PII leaks (audit)
96%Query correctness
Sales ops

Pipeline researcher

Pulls signals from CRM, news, hiring data, GitHub. Drafts outreach with verifiable citations and confidence scores.

+28%Reply rate
−65%Research time
100%Cited sources

Have an agent that "almost works"?

That's the most expensive place to be. Show us your traces — we'll diagnose where the trajectory breaks and propose the smallest set of fixes that gets you to production.

Get a trace audit