AI agents that work in production
We ship tool-using LLMs that handle multi-step workflows your team actually relies on. Instrumented from day one. Cost-bounded. With eval pipelines, not vibes.
What we build
Six categories of agents we have shipped to production. Each comes with observability, cost controls, and eval — not as options, but as baseline requirements.
Conversational Agents
Stateful assistants with memory, tool access, and context management across long sessions. Built on structured conversation graphs — not infinite message arrays that exhaust context windows and fail silently.
Autonomous Workflow Agents
Agents that drive multi-step business processes end to end: data gathering, decision branching, external API calls, and output generation — without a human in the loop for each step. Designed with explicit loop-break conditions and human escalation paths.
Research Agents
Multi-step information synthesis: web search, document retrieval, structured data extraction, cross-source reasoning. We pair these with RAG retrieval when the knowledge base is internal — agents decide when to retrieve and what to retrieve next.
Customer Support Agents
Tier-1 support automation with tool access to your CRM, ticketing system, and knowledge base. Graceful escalation to human agents when confidence is low or intent is outside scope — with full context handoff, not a dead end.
Internal Ops Agents
Agents that handle internal back-office workflows: report generation, data reconciliation, scheduling, compliance checks, Slack-triggered actions. Replaces ad-hoc scripting and manual copy-paste chains with auditable, observable automation.
Integration Agents
Agents that bridge systems that were never designed to talk to each other: ERP to CRM, API to spreadsheet, legacy SOAP endpoints to modern REST surfaces. We use MCP (Model Context Protocol) where it reduces integration surface area — your existing teams publish tool endpoints, the agent discovers and uses them.
Engineering stack
The tools we actually use — chosen per project, not because they trend on Twitter. No framework loyalty, no hidden vendor dependency.
Orchestration Frameworks
LangGraph for stateful graph-based workflows with explicit state machines and human-in-the-loop nodes. CrewAI for role-based multi-agent delegation. AutoGen for conversational agent loops. Raw OpenAI or Anthropic SDK for latency-critical paths where framework overhead is unacceptable. We know when to use each — and when to use none.
Tool Calling
OpenAI function calling and Anthropic tool use as the core primitives. We design tool schemas that minimize hallucinated calls — tight argument typing, enum constraints, clear descriptions. Every tool is independently unit-tested before it enters the agent loop. Parallel tool calls where supported; sequential where ordering matters.
State Management
Agent state lives in Redis (short-term, session-scoped) or Postgres (persistent, auditable). LangGraph checkpointers for mid-run interruption and resume. No in-memory state that evaporates on restart. State schema is versioned — upgrades don't corrupt running sessions.
MCP — Model Context Protocol
We expose internal systems as MCP servers: your database, internal APIs, file systems, SaaS tools. Any compliant agent runtime can then discover and call those endpoints without bespoke glue code per integration. Reduces integration surface area at scale and lets your platform team publish new capabilities independently of the agent layer.
Observability
LangSmith for full trace capture: every LLM call, tool invocation, token count, and latency per step. Helicone for provider-level cost tracking and rate limiting. Custom dashboards for business-level metrics: task completion rate, escalation rate, average steps per run. Nothing ships to production without traces on by default.
Evaluation Pipelines
Task-level eval sets built from real scenarios before POC begins. Unit tests per tool. Integration tests that replay full agent traces against expected outcomes. LLM-as-judge for open-ended output quality. Regression gates block deployment if task completion rate drops below threshold. Eval is not an afterthought.
Patterns we use — and when
Agent architecture is not one pattern. The choice of reasoning loop, control structure, and collaboration model has direct consequences on latency, cost, reliability, and debuggability. We pick deliberately.
Honest disclaimer: most projects don't need a full autonomous agent. If your workflow can be expressed as a deterministic DAG with 1-2 LLM steps embedded, that is almost always the better choice — lower cost, lower latency, easier to test, easier to operate. We will tell you this straight before we scope the engagement.
"An agent is justified when the decision about which steps to take — and in what order — requires reasoning that you cannot pre-encode. If you can write the flowchart, write the flowchart."
Failure modes we solve
We name these upfront. Vendors who don't are either not shipping to production or are not planning to maintain what they build.
Prompt injection via tool outputs
An adversarial string in a retrieved document or API response hijacks the agent's next action. Fix: output sanitization before injection into context, tool output schemas that strip free-text fields from untrusted sources, and sandboxed tool execution environments.
Infinite loops and runaway cost
Agent loops without a clear termination condition run until the token budget or timeout kills them — after you've spent $40 on a task worth $0.02. Fix: hard step limits per run, per-session spend ceilings enforced at the proxy layer, and loop-detection heuristics on repeated tool calls with identical arguments.
Hallucinated tool calls
The LLM calls a tool with plausible-sounding but invalid arguments — the tool fails, the agent hallucinates a successful result and continues. Fix: strict JSON schema validation on every tool call, error injection back into context with structured failure messages, and retry budgets that trigger human escalation on repeated tool failure.
Context window pollution
Long agent runs accumulate tool outputs, intermediate reasoning, and error messages until the context window is saturated — performance degrades and costs spike. Fix: structured summarization of completed sub-tasks, tool output compression, and explicit context pruning strategies per agent pattern.
Evaluation difficulty
Agents produce open-ended outputs across multi-step traces — there's no obvious ground-truth label to compare against. Fix: scenario-based eval sets with explicit success criteria defined upfront, LLM-as-judge scoring calibrated against human ratings, and trace-level assertions on tool call correctness independent of final output quality.
Silent failures in production
The agent completes without error but produces a wrong result — no exception was raised, so no alert fires. Fix: output validation at every agent boundary, business-metric monitoring (not just error rate), and anomaly detection on task completion patterns that flags behavioral drift.
How we ship agent projects
Four phases. Eval gates between each. No milestone where we say "it works on my machine" and call it done.
Discovery
Map the target workflow end to end. Identify which steps require LLM reasoning vs. deterministic logic. Assess risk: what happens if the agent makes a wrong decision? Define human-in-the-loop boundaries before any code is written.
- ✓ Workflow decomposition and risk assessment
- ✓ Tool inventory and integration scoping
- ✓ Agent vs. workflow decision documented
- ✓ Eval scenario set defined upfront
Instrumented POC
We ship a working POC in 2-3 weeks. LangSmith traces on from the first run. Eval scenarios run automatically after every change. We iterate on tool schemas, prompt structure, and loop logic against measured task completion — not developer intuition.
- ✓ Core agent loop + tools wired up
- ✓ Full LangSmith trace from run 1
- ✓ Automated eval on scenario set
- ✓ Cost-per-task and step-count baselines
Production Rollout
Staged rollout: shadow mode first (agent runs but humans act), then partial traffic, then full. Spend ceilings enforced at the proxy. Business-metric monitoring in place before traffic opens. Rollback plan documented and tested.
- ✓ Shadow mode + canary rollout
- ✓ Spend and rate limits enforced
- ✓ Business-metric dashboards live
- ✓ Rollback procedure tested
Iterate
Production data reveals edge cases that scenarios miss. We review traces weekly in the first month, triage failure patterns, and ship targeted fixes — tool schema updates, prompt adjustments, new self-correction conditions. The agent gets better as it runs.
- ✓ Weekly trace reviews in month 1
- ✓ Failure taxonomy and targeted fixes
- ✓ Eval set expanded from real failures
- ✓ Engineering handoff and runbook
When NOT to use agents
If your problem is solvable with a state machine or a deterministic workflow, use that. Agents add latency, cost, and non-determinism. Those are real trade-offs, not footnotes.
You don't need an agent if: your steps are fixed and enumerable, the branching logic can be written as code, the tool calls always happen in the same order, or the main bottleneck is just automating something a human does by clicking through screens. Build a workflow. Add LLM calls where you need text understanding or generation. Done.
You might need an agent if: the right next action genuinely depends on reasoning over intermediate results, the task structure changes based on context you can't pre-encode, or you need the system to handle a large open-ended input space without explicit rules for every case.
We'll tell you straight which bucket your use case falls into — before you spend six weeks building something that should have been a Temporal workflow. Start with an AI audit if you're unsure.
Related resources
Technical reading and adjacent services for teams evaluating agent architectures.
RAG Systems Engineering
Most production agents need retrieval. We engineer the full retrieval pipeline — hybrid search, reranking, evaluation — that makes your agent's knowledge access reliable.
Explore RAG systems → Related ServiceLLM Integration
Agents are LLMs orchestrated. We also ship simpler LLM-powered features — structured outputs, streaming, evals, cost control — when full agents are overkill.
See LLM integration → De-Risk FirstAI Audit
Not sure if agents are the right architecture for your use case? Before committing to a build, get a technical assessment of your problem, constraints, and realistic ROI.
Start with an audit → Framework ComparisonLangGraph vs CrewAI vs AutoGen vs custom
Our opinionated take on agent frameworks — what we ship in production, what we avoid, and what actually matters more than framework choice.
Read the comparison → Technical Deep DiveAgentic RAG — when retrieval needs to reason
Multi-hop queries, iterative retrieval decisions, and tool-calling agents over knowledge bases. The architecture, the trade-offs, and the failure modes in production.
Read the deep-dive → Get StartedEvaluate your agent use case
Bring your workflow, your constraints, and your stack. We will tell you straight whether agents are the right call, what the architecture should look like, and what it realistically takes to ship.
Book a technical call →Technical FAQ
Ship AI agents that work in production
Book a technical call. Bring your workflow, your stack, and your constraints. We will give you a straight answer on whether agents are the right call — and if yes, what the architecture, timeline, and eval strategy should look like.