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Practice

Agentic Systems

Agent runtimes, multi-agent workflows, and MCP-integrated tool use — engineered for enterprises that require them in production.

AI-generated image of an engineering workspace — monitors showing code, traces, and observability dashboards alongside a notebook of architecture sketches.

How it works

Agent runtime topology A central orchestrator (LangGraph or Temporal) connects out to six surrounding subsystems: LLM providers, MCP tool servers, vector retrieval, short-term memory, observability, and human-in-the-loop approvals. A perimeter wraps the whole topology to denote cost governance, circuit breakers, per-team budgets, and the CI eval suite. Cost governance · Circuit breakers · Per-team budgets · Eval suite (CI) Orchestrator LangGraph / Temporal LLM Providers Claude · GPT · Gemini · open-weights MCP Tool Servers CRM · Ticketing · Knowledge · Internal APIs Vector Retrieval pgvector / Qdrant / OpenSearch Short-term Memory Redis Observability OpenTelemetry · Langfuse · Cost dashboards Human-in-the-Loop Approval gates · Escalation chains Every consequential action passes through a logged, eval-tested, cost-bounded path.
Model-agnostic by design — the orchestrator is the only opinionated piece; every other edge is swappable.

What this practice is.

We deliver the operational layer of agentic systems: LangGraph / Temporal orchestration, MCP-native tool integration, vector retrieval (pgvector / Qdrant), OpenTelemetry trace propagation, per-team token budgets, and circuit breakers when a run goes off-rails. A demonstration is not a production deliverable. Every agent we put into production carries a task-success rate, a cost ceiling, and an escalation path. All measured. All documented.

What we build.

Agent orchestration platforms

Multi-agent workflows with planning, memory, tool use, and supervision. Built on LangGraph, CrewAI, or custom orchestration depending on the workload's determinism and latency budget.

MCP-native tool integration

Model Context Protocol servers wrapping client systems — CRMs, ticketing, knowledge bases, internal APIs — with proper auth, audit, and rate-limit policies.

RAG-backed knowledge agents

Retrieval-augmented agents over enterprise corpora — search, summarisation, internal copilots — with citation discipline, eval harnesses, and freshness guarantees.

Eval, observability, and cost governance

Task-success eval suites per agent, structured tracing of every tool call and LLM hop, cost dashboards by team and workflow, and circuit breakers when a run goes off-rails.

How we engineer in this practice.

01

Engineered for production, not demonstration

Every agent we build clears an evaluation suite and a documented cost ceiling before it goes live. Demonstration-grade output is not a production deliverable.

02

Human in the loop for consequential actions

Agents propose; humans approve. Sending an email, modifying a record, executing a refund — all require a human gate unless the buyer signs a documented exception for that action class.

03

Model-agnostic, vendor-neutral

We design so a model swap is a configuration change. Claude, GPT, Gemini, open-weights — the orchestration, evals, and tool layer don't care.

04

Observable end-to-end

Every LLM call, tool call, and decision is logged with context and outcome. If you can't reconstruct why an agent did something, the agent shouldn't be in production.

05

Cost is a first-class SLO

We commit to cost-per-task ceilings as part of the engagement, the same way we commit to accuracy in safety vision. Token spend is engineered, not absorbed.

06

Audit-ready by design

Compliance posture engineered in from the architecture, not retrofitted before the audit. SOC 2 control mapping for the agent runtime, sector-specific obligations where they apply (RBI for fintech workflows, IRDAI for insurance, MeitY guidance for public-data interactions), DPDP for personal data, GDPR for any cross-border flow. Every consequential agent action lands in an immutable audit log the buyer's compliance team can read directly.

Stack in this practice.

  • LangGraph, CrewAI, and custom orchestration runtimes
  • Model Context Protocol (MCP) servers and clients
  • Anthropic Claude, OpenAI, Google Gemini, open-weights via vLLM / TGI
  • Postgres + pgvector or Qdrant for retrieval; Redis for state
  • OpenTelemetry traces, Langfuse / custom dashboards for agent observability
  • Kafka for event-driven workflows; Temporal for long-running orchestration

See the firm-wide stack →

What we won't build in this practice.

Fully autonomous agents on critical operations

Anything that touches money movement, customer accounts, or production systems must pass through human approval, an audit log, and a reversible action layer.

Agents that bypass logging or eval

If you can't observe an agent's behaviour or measure its task-success rate, you can't operate it responsibly.

Synthetic-persona customer-facing agents that deny being AI

Disclosure is non-negotiable. An agent that lies about being an agent fails an integrity test we won't engineer around.

Run a agentic scoping conversation.

Tell us what you've already tried, what you've ruled out, and what success looks like. We come back within one working day.

Request a Discovery Call See our approach