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.

7×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 →