Agentic architecture, eval-first delivery, hardened guardrails, live observability and operational governance — the technical playbook behind every Shakan engagement.
Written for the senior engineer or technical buyer who needs to know how it actually works before signing.
Operating Principle
“We don’t sell tooling. We design and ship the system that makes the tooling produce a measurable business outcome.”
Every engagement runs through the same structured path — so quality is reproducible, not heroic.
System mapping across your existing stack, ROI hypotheses tied to a measurable revenue or cost line, eval-harness scoping, and a written architecture brief before a single line of agent code is committed.
Golden datasets, regression sets and a scoring rubric are assembled before any build work begins. If we cannot describe ‘good output’ in a test, we cannot ship it.
Vertical-slice delivery: a thin end-to-end path lands first, then breadth. You approve scope at the gate between every phase — no Big Bang releases, no surprise invoices.
Canary release behind a feature flag, fallback chains wired up, traces and dashboards live from day one. We watch the first 100 real interactions side-by-side with your team.
Every prompt change, every model upgrade and every tool revision triggers the regression suite. Output drift, latency drift and cost drift are tracked weekly, not anecdotally.
Runbooks, source escrow, prompt registry walkthrough and live training with your engineering or operations team. The goal is that your organisation can operate the system without us.
We architect agents as typed state machines, not chains of hopeful prompts. That choice drives everything downstream.
LangChain is excellent for composing primitives. For stateful, multi-step flows that must be observable, replayable and testable, we lean on LangGraph: typed state, deterministic transitions, checkpointing, and first-class human-in-the-loop nodes. LangChain components still live inside LangGraph nodes where useful.
For simple linear chains, LangChain alone is the right call. We pick the tool that fits the workload, not the trend cycle.
Each node has typed input / output state, an owning prompt version, a fallback path and an entry in the audit log. Tool-use grounding lives at the Tool Executor; refusal and schema validation live at the Validator.
If you can’t describe ‘good output’ as a test, you can’t ship it. Evals are the contract between you, us and the model.
50–500 examples per intent, curated with subject-matter experts. Versioned in git. Treated as production code.
Run on every prompt change, model upgrade or tool revision. CI blocks the merge if regressions exceed the agreed threshold.
Structured comparisons between Claude Sonnet 4.6, Opus 4.7, Haiku 4.5, GPT-4o, o1 and DeepSeek-V3 / R1 — scored on the same rubric.
Measured against grounded sources, not vibes. Reported per intent and per model with confidence intervals where sample size allows.
Did the agent select the right tool, with the right arguments, in the right order? Scored per turn against expected traces.
p50 / p95 / p99 latency budgets, cost-per-conversation tracked per agent, per tenant and per model — surfaced in dashboards.
Deterministic snapshot tests cover prompts whose outputs must not drift unexpectedly — useful for legal, clinical or claim-sensitive copy. A snapshot failure is a deliberate decision point, not an outage.
Defence in depth. Validation, refusal patterns, escalation paths and fallback chains designed in — not retrofitted.
Every structured output is validated through Zod (TypeScript) or Pydantic (Python). Invalid outputs trigger a retry-with-feedback loop, not a silent failure.
Pre- and post-call filters for unsafe content, plus deterministic refusal patterns the model can fall back to rather than hallucinating an answer.
Confidence floors and policy triggers route ambiguous decisions to a human reviewer with full context — not an apology dialog box.
Sonnet 4.6 → Haiku 4.5 → static response, or vendor-A → vendor-B → cached. The system stays graceful when a provider is degraded.
For content systems, factual claims are checked against source-of-truth documents before publication. No quiet fabrication.
Inbound text is scanned for PII and prompt-injection patterns before it reaches the model. Outbound text is scanned for accidental leakage.
Production AI without observability is just a confident demo. We instrument from the first commit.
LangSmith, OpenTelemetry and Helicone-style tracing across the agent graph. Every tool call, every retry, every token cost — captured.
p50 / p95 / p99 per node and end-to-end. Alerts trigger before users notice, not after the support tickets arrive.
Tracked per agent, per tenant, per model. Anomalies on cost are treated as P2 incidents — runaway tokens are a real production risk.
Output distribution monitoring flags when behaviour shifts after a model swap, a prompt edit or an upstream data change.
A sampled stream of conversations is routed to human reviewers — used to feed regression sets, catch novel failure modes and tune confidence floors.
Where the use case warrants, end-users see confidence signals, source citations or ‘why this answer’ summaries.
The same change-management rigour you expect from any production system — applied to prompts, models and agents.
Every agent action — tool call, write, escalation, refusal — is logged with input, output, model version and prompt hash.
Data residency, retention windows, masking rules and access controls are written into the architecture, not bolted on after launch.
When to choose Claude vs GPT vs open-weight: documented per workload, with cost, quality, latency and compliance tradeoffs made explicit.
Every prompt versioned, diffed, code-reviewed and tied to the eval run that approved it. No more ‘someone edited it in the UI on Friday’.
Agent changes follow a structured workflow: eval delta → staging → canary → production. Same rigour as any other production system.
HIPAA, AHPRA, GDPR and AFSL touchpoints are designed in where the vertical demands it — and verified during the audit phase.
Tooling is chosen per workload — not from a preferred-vendor list. We optimise for fit, then for cost, then for vendor stability.
Three worked examples — problem, architecture, eval setup, outcome.
Reception team missing 30%+ of inbound calls outside business hours; bookings leaking to competitors.
Retell AI front-end, LangGraph state machine for triage and booking, Claude Sonnet 4.6 for clinical-tone responses, Haiku 4.5 fallback for cost control, AHPRA-aware refusal patterns.
200-example golden set covering symptom triage edge cases, appointment-type routing, escalation triggers; weekly regression run.
After-hours bookings recovered; human reception now handles only escalations and high-complexity calls.
Senior staff burning 10+ hours per week on document review, status reports and cross-system reconciliation.
n8n + LangGraph orchestration, Opus 4.7 for review tasks, GPT-4o-mini for cheap classification, Pydantic schemas on every structured output.
Snapshot tests for report formats, regression set for classification accuracy, monthly human-review sampling.
Reclaimed senior capacity redeployed to client work; reporting cadence moved from weekly to on-demand.
Content team unable to keep pace with channel demand; quality inconsistent across writers and weeks.
Multi-agent LangGraph (research → outline → draft → claim-check → edit), Claude Opus 4.7 for long-form, Sonnet 4.6 for editing passes, retrieval grounded against a curated source corpus.
Claim-safety rubric, brand-voice scoring, deterministic prompt snapshots; failed claims block publication.
Publishing cadence sustained without quality regression; editor time focused on strategy and final approval.
LangChain is a great toolbox of primitives, but for stateful, multi-step agent flows we need explicit state machines — typed state, deterministic transitions, checkpointing and replay. LangGraph gives us that. We still use LangChain components inside LangGraph nodes where it makes sense; it is not an either/or choice. For simple linear chains, LangChain alone is often enough.
Quality is measured against a versioned golden dataset of 50–500 examples per intent, scored on a rubric that mixes deterministic checks (schema validity, tool selection, citation presence) with model-graded checks (helpfulness, tone, factual grounding). The same rubric runs in CI, in staging and on sampled production traffic.
Every production agent has a documented fallback chain. A typical pattern: primary on Claude Sonnet 4.6, secondary on Haiku 4.5, tertiary on GPT-4o-mini, final fallback to a static deterministic response that says ‘we’re routing you to a human’ and escalates. Health checks and circuit breakers decide when to fail over, not the user’s patience.
Yes, where the workload justifies it. Open-weight models such as Llama 3 and Gemma can be hosted on your own infrastructure for compliance, residency or cost reasons. We will tell you honestly when self-hosting hurts quality more than it helps — and we will not architect around it if the tradeoff is not worth it.
Defence in depth. Inbound text is scanned for known injection patterns and stripped of role-confusing tokens before reaching the model. System prompts are isolated from user input. Tools have allowlists, not blanket capabilities. Sensitive operations require structured confirmation. And we treat every model as untrusted — its outputs are validated before they trigger downstream actions.
Typically 3–8% of total model spend, depending on sampling rate and retention. We sample 100% of failed and low-confidence traces, plus a configurable percentage of successful traffic. Logs are tiered: hot for 30 days, cold for the retention window your compliance regime requires.
Every change — prompt, model, tool — ships behind a feature flag. We route a small percentage of traffic to the new version, compare evals and production metrics against the control, and only promote when the deltas are within the agreed thresholds. If regressions appear, we roll back at the flag, not at the deploy.
Architecture diagrams, runbooks for every failure mode we have observed, the prompt registry, the eval harness, dashboards, on-call playbooks, model-selection rationale, source escrow if requested, and a training session with your team. You own the system end-to-end on day one of handover.
45 minutes with a senior architect. We’ll walk your existing or proposed system, identify the failure modes worth fixing first, and show you what an eval harness for your workload looks like.