The capabilities enterprise AI actually needs.
Eight foundational primitives, built into every Herantes engagement. Skip the glue code. Skip the research spike. Get to production with the architecture the industry is converging on.
The primitives we build every engagement on.
Every engagement ships with these baked in, tested against your data, and observable from day one. No slideware, no reference-architecture copy-paste.
Model Context Protocol, built in from day one
MCP-native integration
MCP hit 97 million monthly SDK downloads in March, reached the Linux Foundation as an open standard, and became the shared plug between Anthropic, OpenAI, Google, and Microsoft agent runtimes. We publish MCP servers for your systems of record and consume the ecosystem so your agents integrate in days, not quarters.
Why it matters
97M
MCP monthly downloads
40%
Enterprise apps with agents by EOY
Yes
Linux Foundation standard
Author custom MCP servers
We expose your CRM, ERP, ledger, or proprietary APIs as first-class MCP servers your agents (and your vendors' agents) can call with audited, scoped permissions.
Consume any MCP tool
The growing MCP ecosystem gets plugged straight into your workflow, from search to browser automation to dev tooling, without brittle wrappers.
Enterprise auth baked in
SSO, OAuth 2.1, scoped tokens, and zero-trust gateway behavior so MCP calls carry the same identity posture as the rest of your stack.
Configuration portability
Portable deployment blueprints move with your environments. No bespoke per-vendor glue, no per-environment drift.
Planner, specialists, critic
Multi-agent orchestration
Gartner logged a 1,445% surge in multi-agent system inquiries between Q1 2024 and Q2 2025, because single all-purpose agents stall on real work. We design small, purpose-built agents that coordinate through shared context: a planner decomposes the task, specialists execute, a critic reviews.
Why it matters
1,445%
Surge in multi-agent inquiries
Always
Purpose-built over monolithic
Typical
Specialist fleets
Decomposition + delegation
A planner agent breaks complex goals into subtasks and delegates to specialists with the right tools, context, and cost budget for each step.
Specialist agent fleets
Narrow, deeply competent agents for each domain: support triage, lead qualification, invoice reconciliation, contract redlining. Small, replaceable, testable.
Shared memory & handoffs
Episodic memory for the current task, long-term memory for customer history. Clean handoffs with full context, no cold transcript dumps.
Cost-aware routing per step
Each step goes to the cheapest model that can solve it. Reasoning-heavy tasks to frontier models, bulk classification to open-weight, voice to latency-tuned.
Dynamic execution, deterministic policy
Agentic workflows with guardrails
Agentic AI is widely adopted but selectively trusted. The difference is guardrails: policy-as-code, irreversible-action approvals, prompt-injection defenses, and the eval-to-guardrail lifecycle that turns every test you run into a production rule.
Why it matters
Automatic
Eval-to-guardrail promotion
OPA / custom
Policy as code
Layered
Prompt-injection defense
Policy-as-code guardrails
Declarative rules for what agents may and may not do, versioned in your repo, enforced at runtime, and validated in CI alongside your other code.
Approval flows for destructive actions
Agents propose refunds, terminations, wires, and schema changes. Humans approve in Slack, Teams, or a queue, and the agent resumes cleanly.
Prompt-injection defenses
Layered input sanitization, tool-use whitelists, content-origin tagging, and model-side safeguards so third-party content can't hijack your agents.
Eval-to-guardrail lifecycle
Every failing eval becomes a guardrail rule, so the agent never regresses on that class of problem. Safety compounds instead of eroding.
RAG over your docs, tickets, and ledgers
Retrieval with citations
Published benchmarks show RAG lifts answer accuracy from 58% to 86% on domain tasks, and a banking chatbot went from 25% to 89% after proper retrieval. Every agent answer traces back to a source document or system record, so your legal team can actually sign off.
Why it matters
86% vs 58%
RAG accuracy (vs base model)
100%
Citations on every answer
25% → 89%
Banking chatbot lift
Hybrid + graph retrieval
Vector, keyword, and graph retrieval combined. Handles conceptual queries, exact-ID lookups, and relationship traversal without a one-size-fits-all embedding.
pgvector, Pinecone, Weaviate
BYO vector store or we run one. Sharded by tenant, versioned, and refreshed on your content cadence so answers never go stale.
Citations on every answer
Each claim links back to the source chunk with offset, document, and version. Compliance loves it, your customers trust it, disputes end faster.
Live re-indexing
Webhooks and change-data-capture from your docs platforms trigger re-indexing so updates propagate in minutes, not batch-nightly.
Test trajectories, not just answers
Production-grade evals
Production evals mean tracing the full trajectory (tool choice, intermediate reasoning, final answer) not just scoring the output. We instrument agents with Phoenix, Langfuse, RAGAS, and Promptfoo, and run regression suites on every change so you catch drift before users do.
Why it matters
Always
Trace-based
On every PR
CI-gated regressions
Both
LLM-judge + rules
Trajectory-level tracing
Every tool call, retry, handoff, and intermediate thought captured. Score the whole path, not just the last line.
LLM-judge + deterministic checks
LLM-as-judge for nuance and subjective quality. Regex, JSON-schema, and rule-based checks for the parts that must be deterministic.
Regression suites in CI
Every PR runs the golden-path eval suite. Agents don't ship until the quality bar holds, and you see the diff per prompt version.
Drift & quality dashboards
Live dashboards on resolution rate, citation rate, escalation rate, and cost per solved task. Trends surface before customer complaints do.
The right model for every step
Model-agnostic routing
Route each task to the model that solves it cheapest and best: Claude for reasoning, open-weight for bulk retrieval, voice-tuned for telephony. No vendor lock-in. No mystery invoices. Swap providers without rewriting a prompt, survive outages without downtime.
Why it matters
Anthropic, OpenAI, Google, Meta
Providers supported
Supported
Self-hosted in VPC
Automatic
Provider fallback
Cost and latency-aware routing
Policy rules decide per-step: cheap open-weight for classification, frontier for reasoning, voice-tuned for telephony. Budgets enforced automatically.
In-VPC self-hosted option
Run open-weight Llama or Mistral models inside your VPC for sensitive workloads. No data leaves your boundary, no per-token billing surprises.
Provider fallback on outage
When a provider has a bad day, traffic reroutes to the next-best model automatically. Your users never know there was an incident.
Prompt portability
Prompts written once, tuned per provider in the router layer. Move to a new model in a day, not a quarter.
Autonomy where it helps, oversight where it matters
Human-in-the-loop governance
Configure the threshold per workflow, per intent, per customer tier. Agents propose, your team dispositions, and every action lands in an immutable audit trail your compliance team can export in a click. This is how agents earn trust.
Why it matters
Configurable
Per-intent thresholds
Slack, Teams, queue
Approval routing
Always
Immutable audit trails
Per-workflow approval thresholds
High-stakes workflows (refunds, contracts, production deploys) route to a human. Low-stakes (greeting, FAQ, data lookup) run autonomously. You set the line.
Slack & Teams approval routing
Approvals land where your team already works. One click to approve, one to redirect, one to audit. Agents wait without blocking users.
Immutable audit trails
Every decision, tool call, and approval stamped with identity and timestamp. Exportable to your SIEM, retained per your policy.
One-click rollback
Surprised by an agent action? Roll back the transaction, the ticket, the email. Agents are undoable by design.
Every decision is replayable
Full observability
Traces, logs, and metrics at node, agent, and workflow level. OpenTelemetry-native so your team stays on the stack you already run. When an incident happens, you replay the conversation, not guess at it.
Why it matters
Yes
OpenTelemetry-native
Per token
Per-step cost tracking
Full
Conversation replay
OpenTelemetry-native traces
Spans for every tool call, model invocation, and handoff. Pipe into Datadog, Honeycomb, Tempo, or whatever your SRE team already runs.
Per-step token & cost tracking
Know exactly what each conversation cost, by step and by model. Budget alarms fire before the invoice does.
Anomaly detection on quality
Statistical baselines on resolution rate, citation rate, and sentiment. Drift triggers pages and rollbacks before it becomes a customer story.
Full conversation replay
Reproduce any session with the exact prompts, tool calls, and model outputs. Debug in hours, not days. Prove compliance in audits.
Also shipped with every engagement
- SOC 2, HIPAA, GDPR-aligned architecture
- Your data stays in your cloud
- No training on customer data
- Full OpenTelemetry traces
- OAuth 2.1 / SSO / zero-trust
- Mutual NDA on request
Not sure which capability fits first? Start with the audit.
30-minute call, no pitch. You walk away with a written readiness scorecard and the two or three capabilities that will move your numbers first.